Population density affects sexual selection in the red flour beetle
Analyses excluding individuals without mating success (treatment specific)
Lennart Winkler1, Ronja
Eilhardt1 & Tim Janicke1,2
1Applied Zoology, Technical University Dresden
2Centre d’Écologie Fonctionnelle et Évolutive, UMR 5175,
CNRS, Université de Montpellier
Last updated: 2022-08-13
Checks: 6 1
Knit directory:
Density_and_sexual_selection_2022/
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Supplementary material reporting R code for the manuscript ‘Population density affects sexual selection in the red flour beetle’.
Load and prepare data
Before we started the analyses, we loaded all necessary packages and data.
#load packages
rm(list = ls())
library(ggeffects)
library(ggplot2)
library(gridExtra)
library(lme4)
library(lmerTest)
library(readr)
library(dplyr)
library(EnvStats)
library(cowplot)
library(gridGraphics)
library(car)
library(RColorBrewer)
library(boot)
library(data.table)
library(base)
library(tidyr)
library(ICC)
#load data
DB_data=read_delim("./data/DB_AllData_V04.CSV",";", escape_double = FALSE, trim_ws = TRUE)
#Set factors and level factors
DB_data$Week=as.factor(DB_data$Week)
DB_data$Date=as.factor(DB_data$Date)
DB_data$Sex=as.factor(DB_data$Sex)
DB_data$Gr_size=as.factor(DB_data$Gr_size)
DB_data$Gr_size <- factor(DB_data$Gr_size, levels=c("SG","LG"))
DB_data$Area=as.factor(DB_data$Area)
#Load Body mass data
DB_BM_female <- read_delim("./data/DB_mass_focals_female.CSV",
";", escape_double = FALSE, trim_ws = TRUE)
DB_BM_male <- read_delim("./data/DB_mass_focals_males.CSV",
";", escape_double = FALSE, trim_ws = TRUE)
DB_data_m=merge(DB_data,DB_BM_male,by.x = 'Well_ID',by.y = 'ID_male_focals')
DB_data_f=merge(DB_data,DB_BM_female,by.x = 'F1_ID',by.y = 'ID_female_focals')
DB_data=rbind(DB_data_m,DB_data_f)
###Exclude incomplete data
DB_data=DB_data[DB_data$excluded!=1,]
#Exclude zero MS (all data)####
DB_data=DB_data[DB_data$MatingPartners_number!=0,]
#Calculate total offspring number ####
DB_data$Total_N_MTP1=colSums(rbind(DB_data$N_MTP1_1,DB_data$N_MTP1_2,DB_data$N_MTP1_3,DB_data$N_MTP1_4,DB_data$N_MTP1_5,DB_data$N_MTP1_6), na.rm = T)
DB_data$Total_N_Rd=colSums(rbind(DB_data$N_RD_1,DB_data$N_RD_2,DB_data$N_RD_3,DB_data$N_RD_4,DB_data$N_RD_5,DB_data$N_RD_6), na.rm = T)/DB_data$N_comp
#Calculate proportional RS ####
#Percentage focal offspring
DB_data$m_prop_RS=NA
DB_data$m_prop_RS=(DB_data$Total_N_MTP1/(DB_data$Total_N_MTP1+DB_data$Total_N_Rd))*100
DB_data$m_prop_RS[DB_data$Sex=='F']=NA
DB_data$f_prop_RS=NA
DB_data$f_prop_RS=(DB_data$Total_N_MTP1/(DB_data$Total_N_MTP1+DB_data$Total_N_Rd))*100
DB_data$f_prop_RS[DB_data$Sex=='M']=NA
#Calculate proportion of successful matings ####
DB_data$Prop_MS=NA
DB_data$Prop_MS=DB_data$Matings_number/(DB_data$Attempts_number+DB_data$Matings_number)
DB_data$Prop_MS[DB_data$Prop_MS==0]=NA
#Calculate total encounters ####
DB_data$Total_Encounters=NA
DB_data$Total_Encounters=DB_data$Attempts_number+DB_data$Matings_number
# Treatment identifier for each density ####
n=1
DB_data$Treatment=NA
for(n in 1:length(DB_data$Sex)){if(DB_data$Gr_size[n]=='SG' && DB_data$Area[n]=='Large'){DB_data$Treatment[n]='D = 0.26'
}else if(DB_data$Gr_size[n]=='LG' && DB_data$Area[n]=='Large'){DB_data$Treatment[n]='D = 0.52'
}else if(DB_data$Gr_size[n]=='SG' && DB_data$Area[n]=='Small'){DB_data$Treatment[n]='D = 0.67'
}else if(DB_data$Gr_size[n]=='LG' && DB_data$Area[n]=='Small'){DB_data$Treatment[n]='D = 1.33'
}else{DB_data$Treatment[n]=NA}}
DB_data$Treatment=as.factor(DB_data$Treatment)
# Exclude Incubator 3 data #### -> poor performance
DB_data_clean=DB_data[DB_data$Incu3!=1,]
# Calculate genetic MS ####
# Only clean data
DB_data_clean$gMS=NA
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_1[i]>=1 & !is.na (DB_data_clean$N_MTP1_1[i])){
DB_data_clean$gMS[i]=1
}else{DB_data_clean$gMS[i]=0}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_2[i]>=1 & !is.na (DB_data_clean$N_MTP1_2[i])){
DB_data_clean$gMS[i]=DB_data_clean$gMS[i]+1
}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_3[i]>=1 & !is.na (DB_data_clean$N_MTP1_3[i])){
DB_data_clean$gMS[i]=DB_data_clean$gMS[i]+1}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_4[i]>=1 & !is.na (DB_data_clean$N_MTP1_4[i])){
DB_data_clean$gMS[i]=DB_data_clean$gMS[i]+1}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_5[i]>=1 & !is.na (DB_data_clean$N_MTP1_5[i])){
DB_data_clean$gMS[i]=DB_data_clean$gMS[i]+1}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_6[i]>=1 & !is.na (DB_data_clean$N_MTP1_6[i])){
DB_data_clean$gMS[i]=DB_data_clean$gMS[i]+1}else{}}
# All data
DB_data$gMS=NA
for(i in 1:length(DB_data$Sex)) {if (DB_data$N_MTP1_1[i]>=1 & !is.na (DB_data$N_MTP1_1[i])){
DB_data$gMS[i]=1
}else{DB_data$gMS[i]=0}}
for(i in 1:length(DB_data$Sex)) {if (DB_data$N_MTP1_2[i]>=1 & !is.na (DB_data$N_MTP1_2[i])){
DB_data$gMS[i]=DB_data$gMS[i]+1
}else{}}
for(i in 1:length(DB_data$Sex)) {if (DB_data$N_MTP1_3[i]>=1 & !is.na (DB_data$N_MTP1_3[i])){
DB_data$gMS[i]=DB_data$gMS[i]+1}else{}}
for(i in 1:length(DB_data$Sex)) {if (DB_data$N_MTP1_4[i]>=1 & !is.na (DB_data$N_MTP1_4[i])){
DB_data$gMS[i]=DB_data$gMS[i]+1}else{}}
for(i in 1:length(DB_data$Sex)) {if (DB_data$N_MTP1_5[i]>=1 & !is.na (DB_data$N_MTP1_5[i])){
DB_data$gMS[i]=DB_data$gMS[i]+1}else{}}
for(i in 1:length(DB_data$Sex)) {if (DB_data$N_MTP1_6[i]>=1 & !is.na (DB_data$N_MTP1_6[i])){
DB_data$gMS[i]=DB_data$gMS[i]+1}else{}}
#Calculate Rd competition RS ####
DB_data_clean$m_RS_Rd_comp=NA
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_1[i]>=1 & !is.na (DB_data_clean$N_MTP1_1[i])){
DB_data_clean$m_RS_Rd_comp[i]=DB_data_clean$N_RD_1[i]
}else{DB_data_clean$m_RS_Rd_comp[i]=0}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_2[i]>=1 & !is.na (DB_data_clean$N_MTP1_2[i])){
DB_data_clean$m_RS_Rd_comp[i]=DB_data_clean$m_RS_Rd_comp[i]+DB_data_clean$N_RD_2[i]
}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_3[i]>=1 & !is.na (DB_data_clean$N_MTP1_3[i])){
DB_data_clean$m_RS_Rd_comp[i]=DB_data_clean$m_RS_Rd_comp[i]+DB_data_clean$N_RD_3[i]
}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_4[i]>=1 & !is.na (DB_data_clean$N_MTP1_4[i])){
DB_data_clean$m_RS_Rd_comp[i]=DB_data_clean$m_RS_Rd_comp[i]+DB_data_clean$N_RD_4[i]
}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_5[i]>=1 & !is.na (DB_data_clean$N_MTP1_5[i])){
DB_data_clean$m_RS_Rd_comp[i]=DB_data_clean$m_RS_Rd_comp[i]+DB_data_clean$N_RD_5[i]
}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_6[i]>=1 & !is.na (DB_data_clean$N_MTP1_6[i])){
DB_data_clean$m_RS_Rd_comp[i]=DB_data_clean$m_RS_Rd_comp[i]+DB_data_clean$N_RD_6[i]
}else{}}
# Check matings of males #### -> add copulations where offspring found but no copulation registered
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_1[i]>=1 && DB_data_clean$Cop_Fe_1[i]==0 & !is.na (DB_data_clean$Cop_Fe_1[i])& !is.na (DB_data_clean$N_MTP1_1[i])){
DB_data_clean$Cop_Fe_1[i]=1}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_2[i]>=1 && DB_data_clean$Cop_Fe_2[i]==0 & !is.na (DB_data_clean$Cop_Fe_2[i])& !is.na (DB_data_clean$N_MTP1_2[i])){
DB_data_clean$Cop_Fe_2[i]=1}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_3[i]>=1 && DB_data_clean$Cop_Fe_3[i]==0 & !is.na (DB_data_clean$Cop_Fe_3[i])& !is.na (DB_data_clean$N_MTP1_3[i])){
DB_data_clean$Cop_Fe_3[i]=1}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_4[i]>=1 && DB_data_clean$Cop_Fe_4[i]==0 & !is.na (DB_data_clean$Cop_Fe_4[i])& !is.na (DB_data_clean$N_MTP1_4[i])){
DB_data_clean$Cop_Fe_4[i]=1}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_5[i]>=1 && DB_data_clean$Cop_Fe_5[i]==0 & !is.na (DB_data_clean$Cop_Fe_5[i])& !is.na (DB_data_clean$N_MTP1_5[i])){
DB_data_clean$Cop_Fe_5[i]=1}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$N_MTP1_6[i]>=1 && DB_data_clean$Cop_Fe_6[i]==0 & !is.na (DB_data_clean$Cop_Fe_6[i])& !is.na (DB_data_clean$N_MTP1_6[i])){
DB_data_clean$Cop_Fe_6[i]=1}else{}}
# Calculate Rd competition RS of all copulations with potential sperm competition with the focal ####
DB_data_clean$m_RS_Rd_comp_full=NA
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$Cop_Fe_1[i]>=1 & !is.na (DB_data_clean$Cop_Fe_1[i])){
DB_data_clean$m_RS_Rd_comp_full[i]=DB_data_clean$N_RD_1[i]
}else{DB_data_clean$m_RS_Rd_comp_full[i]=0}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$Cop_Fe_2[i]>=1 & !is.na (DB_data_clean$Cop_Fe_2[i])){
DB_data_clean$m_RS_Rd_comp_full[i]=DB_data_clean$m_RS_Rd_comp_full[i]+DB_data_clean$N_RD_2[i]
}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$Cop_Fe_3[i]>=1 & !is.na (DB_data_clean$Cop_Fe_3[i])){
DB_data_clean$m_RS_Rd_comp_full[i]=DB_data_clean$m_RS_Rd_comp_full[i]+DB_data_clean$N_RD_3[i]
}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$Cop_Fe_4[i]>=1 & !is.na (DB_data_clean$Cop_Fe_4[i])){
DB_data_clean$m_RS_Rd_comp_full[i]=DB_data_clean$m_RS_Rd_comp_full[i]+DB_data_clean$N_RD_4[i]
}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$Cop_Fe_5[i]>=1 & !is.na (DB_data_clean$Cop_Fe_5[i])){
DB_data_clean$m_RS_Rd_comp_full[i]=DB_data_clean$m_RS_Rd_comp_full[i]+DB_data_clean$N_RD_5[i]
}else{}}
for(i in 1:length(DB_data_clean$Sex)) {if (DB_data_clean$Cop_Fe_6[i]>=1 & !is.na (DB_data_clean$Cop_Fe_6[i])){
DB_data_clean$m_RS_Rd_comp_full[i]=DB_data_clean$m_RS_Rd_comp_full[i]+DB_data_clean$N_RD_6[i]
}else{}}
# Calculate trait values ####
# Males ####
# Total number of matings (all data)
DB_data$m_TotMatings=NA
DB_data$m_TotMatings=DB_data$Matings_number
DB_data$m_TotMatings[DB_data$Sex=='F']=NA
# Avarage mating duration (all data)
DB_data$MatingDuration_av[DB_data$MatingDuration_av==0]=NA
DB_data$m_MatingDuration_av=NA
DB_data$m_MatingDuration_av=DB_data$MatingDuration_av
DB_data$m_MatingDuration_av[DB_data$Sex=='F']=NA
DB_data$MatingDuration_av[DB_data$MatingDuration_av==0]=NA
# Total number of mating attempts (all data)
DB_data$m_Attempts_number=NA
DB_data$m_Attempts_number=DB_data$Attempts_number
DB_data$m_Attempts_number[DB_data$Sex=='F']=NA
# Proportional mating success (all data)
DB_data$m_Prop_MS=NA
DB_data$m_Prop_MS=DB_data$Prop_MS
DB_data$m_Prop_MS[DB_data$Sex=='F']=NA
#Total encounters (all data)
DB_data$m_Total_Encounters=NA
DB_data$m_Total_Encounters=DB_data$Total_Encounters
DB_data$m_Total_Encounters[DB_data$Sex=='F']=NA
# Reproductive success
DB_data_clean$m_RS=NA
DB_data_clean$m_RS=DB_data_clean$Total_N_MTP1
DB_data_clean$m_RS[DB_data_clean$Sex=='F']=NA
# Mating success (number of different partners)
# Clean data
DB_data_clean$m_cMS=NA
DB_data_clean$m_cMS=DB_data_clean$MatingPartners_number
DB_data_clean$m_cMS[DB_data_clean$Sex=='F']=NA
for(i in 1:length(DB_data_clean$m_cMS)) {if (DB_data_clean$gMS[i]>DB_data_clean$m_cMS[i] & !is.na (DB_data_clean$m_cMS[i])){
DB_data_clean$m_cMS[i]=DB_data_clean$gMS[i]}else{}}
# All data
DB_data$m_cMS=NA
DB_data$m_cMS=DB_data$MatingPartners_number
DB_data$m_cMS[DB_data$Sex=='F']=NA
for(i in 1:length(DB_data$m_cMS)) {if (DB_data$gMS[i]>DB_data$m_cMS[i] & !is.na (DB_data$m_cMS[i])){
DB_data$m_cMS[i]=DB_data$gMS[i]}else{}}
# Insemination success
DB_data_clean$m_InSuc=NA
DB_data_clean$m_InSuc=DB_data_clean$gMS/DB_data_clean$m_cMS
for(i in 1:length(DB_data_clean$m_InSuc)) {if (DB_data_clean$m_cMS[i]==0 & !is.na (DB_data_clean$m_cMS[i])){
DB_data_clean$m_InSuc[i]=NA}else{}}
# Fertilization success
DB_data_clean$m_feSuc=NA
DB_data_clean$m_feSuc=DB_data_clean$m_RS/(DB_data_clean$m_RS+DB_data_clean$m_RS_Rd_comp)
for(i in 1:length(DB_data_clean$m_feSuc)) {if (DB_data_clean$m_InSuc[i]==0 | is.na (DB_data_clean$m_InSuc[i])){
DB_data_clean$m_feSuc[i]=NA}else{}}
# Fecundicty of partners
DB_data_clean$m_pFec=NA
DB_data_clean$m_pFec=(DB_data_clean$m_RS+DB_data_clean$m_RS_Rd_comp)/DB_data_clean$gMS
for(i in 1:length(DB_data_clean$m_pFec)) {if (DB_data_clean$gMS[i]==0){
DB_data_clean$m_pFec[i]=NA}else{}}
# Paternity success
DB_data_clean$m_PS=NA
DB_data_clean$m_PS=DB_data_clean$m_RS/(DB_data_clean$m_RS+DB_data_clean$m_RS_Rd_comp_full)
for(i in 1:length(DB_data_clean$m_PS)) {if (DB_data_clean$m_RS[i]==0 & !is.na (DB_data_clean$m_RS[i])){
DB_data_clean$m_PS[i]=NA}else{}}
# Fecundity of partners in all females the focal copulated with
DB_data_clean$m_pFec_compl=NA
DB_data_clean$m_pFec_compl=(DB_data_clean$m_RS+DB_data_clean$m_RS_Rd_comp_full)/DB_data_clean$m_cMS
for(i in 1:length(DB_data_clean$m_pFec)) {if (DB_data_clean$m_cMS[i]==0 & !is.na (DB_data_clean$m_cMS[i])){
DB_data_clean$m_pFec[i]=NA}else{}}
# Females ####
# Total number of matings (all data)
DB_data$f_TotMatings=NA
DB_data$f_TotMatings=DB_data$Matings_number
DB_data$f_TotMatings[DB_data$Sex=='M']=NA
# Avarage mating duration (all data)
DB_data$f_MatingDuration_av=NA
DB_data$f_MatingDuration_av=DB_data$MatingDuration_av
DB_data$f_MatingDuration_av[DB_data$Sex=='M']=NA
DB_data$MatingDuration_av[DB_data$MatingDuration_av==0]=NA
# Total number of mating attempts (all data)
DB_data$f_Attempts_number=NA
DB_data$f_Attempts_number=DB_data$Attempts_number
DB_data$f_Attempts_number[DB_data$Sex=='M']=NA
# Proportional mating success (all data)
DB_data$f_Prop_MS=NA
DB_data$f_Prop_MS=DB_data$Prop_MS
DB_data_clean$f_Prop_MS[DB_data_clean$Sex=='M']=NA
#Total encounters (all data)
DB_data$f_Total_Encounters=NA
DB_data$f_Total_Encounters=DB_data$Total_Encounters
DB_data$f_Total_Encounters[DB_data$Sex=='M']=NA
# Reproductive success
DB_data_clean$f_RS=NA
DB_data_clean$f_RS=DB_data_clean$Total_N_MTP1
DB_data_clean$f_RS[DB_data_clean$Sex=='M']=NA
# Mating success (number of different partners)
# Clean data
DB_data_clean$f_cMS=NA
DB_data_clean$f_cMS=DB_data_clean$MatingPartners_number
DB_data_clean$f_cMS[DB_data_clean$Sex=='M']=NA
for(i in 1:length(DB_data_clean$f_cMS)) {if (DB_data_clean$gMS[i]>DB_data_clean$f_cMS[i] & !is.na (DB_data_clean$f_cMS[i])){
DB_data_clean$f_cMS[i]=DB_data_clean$gMS[i]}else{}}
# All data
DB_data$f_cMS=NA
DB_data$f_cMS=DB_data$MatingPartners_number
DB_data$f_cMS[DB_data$Sex=='M']=NA
for(i in 1:length(DB_data$f_cMS)) {if (DB_data$gMS[i]>DB_data$f_cMS[i] & !is.na (DB_data$f_cMS[i])){
DB_data$f_cMS[i]=DB_data$gMS[i]}else{}}
# Fecundity per mating partner
DB_data_clean$f_fec_pMate=NA
DB_data_clean$f_fec_pMate=DB_data_clean$f_RS/DB_data_clean$f_cMS
for(i in 1:length(DB_data_clean$f_fec_pMate)) {if (DB_data_clean$f_RS[i]==0 & !is.na (DB_data_clean$f_RS[i])){
DB_data_clean$f_fec_pMate[i]=0}else{}}
for(i in 1:length(DB_data_clean$f_fec_pMate)) {if (DB_data_clean$f_cMS[i]==0 & !is.na (DB_data_clean$f_cMS[i])){
DB_data_clean$f_fec_pMate[i]=NA}else{}}
# Relativize data per treatment and sex ####
# Small group + large Area
DB_data_clean_0.26=DB_data_clean[DB_data_clean$Treatment=='D = 0.26',]
DB_data_clean_0.26$rel_m_RS=NA
DB_data_clean_0.26$rel_m_prop_RS=NA
DB_data_clean_0.26$rel_m_cMS=NA
DB_data_clean_0.26$rel_m_InSuc=NA
DB_data_clean_0.26$rel_m_feSuc=NA
DB_data_clean_0.26$rel_m_pFec=NA
DB_data_clean_0.26$rel_m_PS=NA
DB_data_clean_0.26$rel_m_pFec_compl=NA
DB_data_clean_0.26$rel_f_RS=NA
DB_data_clean_0.26$rel_f_prop_RS=NA
DB_data_clean_0.26$rel_f_cMS=NA
DB_data_clean_0.26$rel_f_fec_pMate=NA
DB_data_clean_0.26$rel_m_RS=DB_data_clean_0.26$m_RS/mean(DB_data_clean_0.26$m_RS,na.rm=T)
DB_data_clean_0.26$rel_m_prop_RS=DB_data_clean_0.26$m_prop_RS/mean(DB_data_clean_0.26$m_prop_RS,na.rm=T)
DB_data_clean_0.26$rel_m_cMS=DB_data_clean_0.26$m_cMS/mean(DB_data_clean_0.26$m_cMS,na.rm=T)
DB_data_clean_0.26$rel_m_InSuc=DB_data_clean_0.26$m_InSuc/mean(DB_data_clean_0.26$m_InSuc,na.rm=T)
DB_data_clean_0.26$rel_m_feSuc=DB_data_clean_0.26$m_feSuc/mean(DB_data_clean_0.26$m_feSuc,na.rm=T)
DB_data_clean_0.26$rel_m_pFec=DB_data_clean_0.26$m_pFec/mean(DB_data_clean_0.26$m_pFec,na.rm=T)
DB_data_clean_0.26$rel_m_PS=DB_data_clean_0.26$m_PS/mean(DB_data_clean_0.26$m_PS,na.rm=T)
DB_data_clean_0.26$rel_m_pFec_compl=DB_data_clean_0.26$m_pFec_compl/mean(DB_data_clean_0.26$m_pFec_compl,na.rm=T)
DB_data_clean_0.26$rel_f_RS=DB_data_clean_0.26$f_RS/mean(DB_data_clean_0.26$f_RS,na.rm=T)
DB_data_clean_0.26$rel_f_prop_RS=DB_data_clean_0.26$f_prop_RS/mean(DB_data_clean_0.26$f_prop_RS,na.rm=T)
DB_data_clean_0.26$rel_f_cMS=DB_data_clean_0.26$f_cMS/mean(DB_data_clean_0.26$f_cMS,na.rm=T)
DB_data_clean_0.26$rel_f_fec_pMate=DB_data_clean_0.26$f_fec_pMate/mean(DB_data_clean_0.26$f_fec_pMate,na.rm=T)
# Large group + large Area
DB_data_clean_0.52=DB_data_clean[DB_data_clean$Treatment=='D = 0.52',]
#Relativize data
DB_data_clean_0.52$rel_m_RS=NA
DB_data_clean_0.52$rel_m_prop_RS=NA
DB_data_clean_0.52$rel_m_cMS=NA
DB_data_clean_0.52$rel_m_InSuc=NA
DB_data_clean_0.52$rel_m_feSuc=NA
DB_data_clean_0.52$rel_m_pFec=NA
DB_data_clean_0.52$rel_m_PS=NA
DB_data_clean_0.52$rel_m_pFec_compl=NA
DB_data_clean_0.52$rel_f_RS=NA
DB_data_clean_0.52$rel_f_prop_RS=NA
DB_data_clean_0.52$rel_f_cMS=NA
DB_data_clean_0.52$rel_f_fec_pMate=NA
DB_data_clean_0.52$rel_m_RS=DB_data_clean_0.52$m_RS/mean(DB_data_clean_0.52$m_RS,na.rm=T)
DB_data_clean_0.52$rel_m_prop_RS=DB_data_clean_0.52$m_prop_RS/mean(DB_data_clean_0.52$m_prop_RS,na.rm=T)
DB_data_clean_0.52$rel_m_cMS=DB_data_clean_0.52$m_cMS/mean(DB_data_clean_0.52$m_cMS,na.rm=T)
DB_data_clean_0.52$rel_m_InSuc=DB_data_clean_0.52$m_InSuc/mean(DB_data_clean_0.52$m_InSuc,na.rm=T)
DB_data_clean_0.52$rel_m_feSuc=DB_data_clean_0.52$m_feSuc/mean(DB_data_clean_0.52$m_feSuc,na.rm=T)
DB_data_clean_0.52$rel_m_pFec=DB_data_clean_0.52$m_pFec/mean(DB_data_clean_0.52$m_pFec,na.rm=T)
DB_data_clean_0.52$rel_m_PS=DB_data_clean_0.52$m_PS/mean(DB_data_clean_0.52$m_PS,na.rm=T)
DB_data_clean_0.52$rel_m_pFec_compl=DB_data_clean_0.52$m_pFec_compl/mean(DB_data_clean_0.52$m_pFec_compl,na.rm=T)
DB_data_clean_0.52$rel_f_RS=DB_data_clean_0.52$f_RS/mean(DB_data_clean_0.52$f_RS,na.rm=T)
DB_data_clean_0.52$rel_f_prop_RS=DB_data_clean_0.52$f_prop_RS/mean(DB_data_clean_0.52$f_prop_RS,na.rm=T)
DB_data_clean_0.52$rel_f_cMS=DB_data_clean_0.52$f_cMS/mean(DB_data_clean_0.52$f_cMS,na.rm=T)
DB_data_clean_0.52$rel_f_fec_pMate=DB_data_clean_0.52$f_fec_pMate/mean(DB_data_clean_0.52$f_fec_pMate,na.rm=T)
# Small group + small Area
DB_data_clean_0.67=DB_data_clean[DB_data_clean$Treatment=='D = 0.67',]
#Relativize data
DB_data_clean_0.67$rel_m_RS=NA
DB_data_clean_0.67$rel_m_prop_RS=NA
DB_data_clean_0.67$rel_m_cMS=NA
DB_data_clean_0.67$rel_m_InSuc=NA
DB_data_clean_0.67$rel_m_feSuc=NA
DB_data_clean_0.67$rel_m_pFec=NA
DB_data_clean_0.67$rel_m_PS=NA
DB_data_clean_0.67$rel_m_pFec_compl=NA
DB_data_clean_0.67$rel_f_RS=NA
DB_data_clean_0.67$rel_f_prop_RS=NA
DB_data_clean_0.67$rel_f_cMS=NA
DB_data_clean_0.67$rel_f_fec_pMate=NA
DB_data_clean_0.67$rel_m_RS=DB_data_clean_0.67$m_RS/mean(DB_data_clean_0.67$m_RS,na.rm=T)
DB_data_clean_0.67$rel_m_prop_RS=DB_data_clean_0.67$m_prop_RS/mean(DB_data_clean_0.67$m_prop_RS,na.rm=T)
DB_data_clean_0.67$rel_m_cMS=DB_data_clean_0.67$m_cMS/mean(DB_data_clean_0.67$m_cMS,na.rm=T)
DB_data_clean_0.67$rel_m_InSuc=DB_data_clean_0.67$m_InSuc/mean(DB_data_clean_0.67$m_InSuc,na.rm=T)
DB_data_clean_0.67$rel_m_feSuc=DB_data_clean_0.67$m_feSuc/mean(DB_data_clean_0.67$m_feSuc,na.rm=T)
DB_data_clean_0.67$rel_m_pFec=DB_data_clean_0.67$m_pFec/mean(DB_data_clean_0.67$m_pFec,na.rm=T)
DB_data_clean_0.67$rel_m_PS=DB_data_clean_0.67$m_PS/mean(DB_data_clean_0.67$m_PS,na.rm=T)
DB_data_clean_0.67$rel_m_pFec_compl=DB_data_clean_0.67$m_pFec_compl/mean(DB_data_clean_0.67$m_pFec_compl,na.rm=T)
DB_data_clean_0.67$rel_f_RS=DB_data_clean_0.67$f_RS/mean(DB_data_clean_0.67$f_RS,na.rm=T)
DB_data_clean_0.67$rel_f_prop_RS=DB_data_clean_0.67$f_prop_RS/mean(DB_data_clean_0.67$f_prop_RS,na.rm=T)
DB_data_clean_0.67$rel_f_cMS=DB_data_clean_0.67$f_cMS/mean(DB_data_clean_0.67$f_cMS,na.rm=T)
DB_data_clean_0.67$rel_f_fec_pMate=DB_data_clean_0.67$f_fec_pMate/mean(DB_data_clean_0.67$f_fec_pMate,na.rm=T)
# Large group + small Area
DB_data_clean_1.33=DB_data_clean[DB_data_clean$Treatment=='D = 1.33',]
#Relativize data
DB_data_clean_1.33$rel_m_RS=NA
DB_data_clean_1.33$rel_m_prop_RS=NA
DB_data_clean_1.33$rel_m_cMS=NA
DB_data_clean_1.33$rel_m_InSuc=NA
DB_data_clean_1.33$rel_m_feSuc=NA
DB_data_clean_1.33$rel_m_pFec=NA
DB_data_clean_1.33$rel_m_PS=NA
DB_data_clean_1.33$rel_m_pFec_compl=NA
DB_data_clean_1.33$rel_f_RS=NA
DB_data_clean_1.33$rel_f_prop_RS=NA
DB_data_clean_1.33$rel_f_cMS=NA
DB_data_clean_1.33$rel_f_fec_pMate=NA
DB_data_clean_1.33$rel_m_RS=DB_data_clean_1.33$m_RS/mean(DB_data_clean_1.33$m_RS,na.rm=T)
DB_data_clean_1.33$rel_m_prop_RS=DB_data_clean_1.33$m_prop_RS/mean(DB_data_clean_1.33$m_prop_RS,na.rm=T)
DB_data_clean_1.33$rel_m_cMS=DB_data_clean_1.33$m_cMS/mean(DB_data_clean_1.33$m_cMS,na.rm=T)
DB_data_clean_1.33$rel_m_InSuc=DB_data_clean_1.33$m_InSuc/mean(DB_data_clean_1.33$m_InSuc,na.rm=T)
DB_data_clean_1.33$rel_m_feSuc=DB_data_clean_1.33$m_feSuc/mean(DB_data_clean_1.33$m_feSuc,na.rm=T)
DB_data_clean_1.33$rel_m_pFec=DB_data_clean_1.33$m_pFec/mean(DB_data_clean_1.33$m_pFec,na.rm=T)
DB_data_clean_1.33$rel_m_PS=DB_data_clean_1.33$m_PS/mean(DB_data_clean_1.33$m_PS,na.rm=T)
DB_data_clean_1.33$rel_m_pFec_compl=DB_data_clean_1.33$m_pFec_compl/mean(DB_data_clean_1.33$m_pFec_compl,na.rm=T)
DB_data_clean_1.33$rel_f_RS=DB_data_clean_1.33$f_RS/mean(DB_data_clean_1.33$f_RS,na.rm=T)
DB_data_clean_1.33$rel_f_prop_RS=DB_data_clean_1.33$f_prop_RS/mean(DB_data_clean_1.33$f_prop_RS,na.rm=T)
DB_data_clean_1.33$rel_f_cMS=DB_data_clean_1.33$f_cMS/mean(DB_data_clean_1.33$f_cMS,na.rm=T)
DB_data_clean_1.33$rel_f_fec_pMate=DB_data_clean_1.33$f_fec_pMate/mean(DB_data_clean_1.33$f_fec_pMate,na.rm=T)
# Set colors for figures
colpal=brewer.pal(4, 'Dark2')
colpal2=brewer.pal(3, 'Set1')
colpal3=brewer.pal(4, 'Paired')
slava_ukrajini=(c('#0057B8','#FFD700'))
colorESEB=c('#01519c','#ffdf33')
colorESEB2=c('#1DA1F2','#ffec69')
# Merge data according to treatment #### -> Reduce treatments to area and population size
#Area
DB_data_clean_Large_area=rbind(DB_data_clean_0.26,DB_data_clean_0.52)
DB_data_clean_Small_area=rbind(DB_data_clean_0.67,DB_data_clean_1.33)
#Population size
DB_data_clean_Small_pop=rbind(DB_data_clean_0.26,DB_data_clean_0.67)
DB_data_clean_Large_pop=rbind(DB_data_clean_0.52,DB_data_clean_1.33)
# Merge data according to treatment full data set #### -> Reduce treatments to area and population size
DB_data_0.26=DB_data[DB_data$Treatment=='D = 0.26',]
DB_data_0.52=DB_data[DB_data$Treatment=='D = 0.52',]
DB_data_0.67=DB_data[DB_data$Treatment=='D = 0.67',]
DB_data_1.33=DB_data[DB_data$Treatment=='D = 1.33',]
#Area
DB_data_Large_area_full=rbind(DB_data_0.26,DB_data_0.52)
DB_data_Small_area_full=rbind(DB_data_0.67,DB_data_1.33)
#Population size
DB_data_Small_pop_full=rbind(DB_data_0.26,DB_data_0.67)
DB_data_Large_pop_full=rbind(DB_data_0.52,DB_data_1.33)Treatment effects
Mating behaviour
We first tested the effect that the density treatments had on the
mating behaviour of focal beetles.
Behavioural variables:
-
Number of matings
- Number of different mating partners (mating
success)
- Mating duration in seconds
- Mating encounters
(mating number + mating attempts)
- Proportion of successful matings
(mating number/mating number + mating attempts)
Number of matings
p2<-ggplot(DB_data, aes(x=Sex, y=as.numeric(Matings_number),fill=Treatment, col=Treatment)) +
geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=0.9),shape=19, alpha=0.75, size = 2)+
stat_summary(fun.min = function(z) { quantile(z,0.25) },
fun.max = function(z) { quantile(z,0.75) },
fun = mean,position=position_dodge(.9), size = 0.9,col='black',show.legend = F)+
scale_color_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))+
scale_fill_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))+
xlab('Sex')+ylab("Number of matings")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,12)+labs(tag = "")+
annotate("text",label='n =',x=0.5,y=12,size=4)+
annotate("text",label='33',x=.65,y=12,size=4)+
annotate("text",label='53',x=.88,y=12,size=4)+
annotate("text",label='41',x=1.11,y=12,size=4)+
annotate("text",label='38',x=1.34,y=12,size=4)+
annotate("text",label='50',x=1.65,y=12,size=4)+
annotate("text",label='38',x=1.88,y=12,size=4)+
annotate("text",label='35',x=2.11,y=12,size=4)+
annotate("text",label='47',x=2.34,y=12,size=4)+
theme(panel.border = element_blank(),
plot.margin = margin(0.1,2,0,0.2,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(1, 0.8),
plot.tag.position=c(0.01,0.98),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
guides(colour = guide_legend(override.aes = list(size=4)))
p2
Figure 1: Effects of density treatments on the number of matings of
female and male focals. Black bars indicate means and quartile
borders.
Statistical models: Number of matings (quasi-Poisson
GLM)
Effect of density on number of matings in females.
mod4.1=glm(f_TotMatings~Gr_size*Area,data=DB_data,family = quasipoisson)
summary(mod4.1)
Call:
glm(formula = f_TotMatings ~ Gr_size * Area, family = quasipoisson,
data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5649 -1.0337 -0.2763 0.3408 3.6965
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.24360 0.09338 13.318 <2e-16 ***
Gr_sizeLG -0.36121 0.18228 -1.982 0.0497 *
AreaSmall -0.21168 0.15829 -1.337 0.1835
Gr_sizeLG:AreaSmall 0.28755 0.26274 1.094 0.2759
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 1.421324)
Null deviance: 164.51 on 129 degrees of freedom
Residual deviance: 156.63 on 126 degrees of freedom
(148 Beobachtungen als fehlend gelöscht)
AIC: NA
Number of Fisher Scoring iterations: 5Anova(mod4.1,type=3) #If the interactions are not significant, type II gives a more powerful test.Analysis of Deviance Table (Type III tests)
Response: f_TotMatings
LR Chisq Df Pr(>Chisq)
Gr_size 4.1427 1 0.04181 *
Area 1.8241 1 0.17683
Gr_size:Area 1.2065 1 0.27203
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(mod4.1,type=2)Analysis of Deviance Table (Type II tests)
Response: f_TotMatings
LR Chisq Df Pr(>Chisq)
Gr_size 3.08811 1 0.07887 .
Area 0.74886 1 0.38684
Gr_size:Area 1.20648 1 0.27203
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Effect of density on number of matings in males.
mod3.1=glm(m_TotMatings~Gr_size*Area,data=DB_data,family = quasipoisson)
summary(mod3.1)
Call:
glm(formula = m_TotMatings ~ Gr_size * Area, family = quasipoisson,
data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.4493 -0.9639 -0.1994 0.4255 3.0182
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.1689931 0.1019747 11.464 <2e-16 ***
Gr_sizeLG -0.3380622 0.1449262 -2.333 0.0211 *
AreaSmall 0.0002368 0.1360628 0.002 0.9986
Gr_sizeLG:AreaSmall 0.0389102 0.2087867 0.186 0.8524
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 1.07108)
Null deviance: 150.57 on 147 degrees of freedom
Residual deviance: 139.99 on 144 degrees of freedom
(130 Beobachtungen als fehlend gelöscht)
AIC: NA
Number of Fisher Scoring iterations: 5Anova(mod3.1,type=3) #If the interactions are not significant, type II gives a more powerful test.Analysis of Deviance Table (Type III tests)
Response: m_TotMatings
LR Chisq Df Pr(>Chisq)
Gr_size 5.4215 1 0.01989 *
Area 0.0000 1 0.99861
Gr_size:Area 0.0347 1 0.85223
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(mod3.1,type=2)Analysis of Deviance Table (Type II tests)
Response: m_TotMatings
LR Chisq Df Pr(>Chisq)
Gr_size 9.4478 1 0.002114 **
Area 0.0263 1 0.871205
Gr_size:Area 0.0347 1 0.852230
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Number of mating partners
p3<-ggplot(DB_data, aes(x=Sex, y=as.numeric(MatingPartners_number),fill=Treatment, col=Treatment)) +
geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=0.9),shape=19, alpha=0.75, size = 2)+
stat_summary(fun.min = function(z) { quantile(z,0.25) },
fun.max = function(z) { quantile(z,0.75) },
fun = mean,position=position_dodge(.9), size = 0.9,col='black',show.legend = F)+
scale_color_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))+
scale_fill_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))+
xlab('Sex')+ylab("Number of partners")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,5.4)+labs(tag = "")+
annotate("text",label='n =',x=0.5,y=5.4,size=4)+
annotate("text",label='33',x=.65,y=5.4,size=4)+
annotate("text",label='53',x=.88,y=5.4,size=4)+
annotate("text",label='41',x=1.11,y=5.4,size=4)+
annotate("text",label='38',x=1.34,y=5.4,size=4)+
annotate("text",label='50',x=1.65,y=5.4,size=4)+
annotate("text",label='38',x=1.88,y=5.4,size=4)+
annotate("text",label='35',x=2.11,y=5.4,size=4)+
annotate("text",label='47',x=2.34,y=5.4,size=4)+
theme(panel.border = element_blank(),
plot.margin = margin(0,2,0,0.2,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(1, 0.8),
plot.tag.position=c(0.01,0.98),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
guides(colour = guide_legend(override.aes = list(size=4)))
p3
Figure 2: Effects of density treatments on the number of mating partners
of female and male focals. Black bars indicate means and quartile
borders.
Statistical models: Number of mating partners (quasi-Poisson
GLM)
Effect of density on number of mating partners in females.
mod6.1=glm(f_cMS~Gr_size*Area,data=DB_data,family = quasipoisson)
summary(mod6.1)
Call:
glm(formula = f_cMS ~ Gr_size * Area, family = quasipoisson,
data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.8726 -0.6770 0.0151 0.2937 1.8498
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.68245 0.06400 10.663 <2e-16 ***
Gr_sizeLG -0.03186 0.11125 -0.286 0.7750
AreaSmall -0.20442 0.10823 -1.889 0.0612 .
Gr_sizeLG:AreaSmall 0.31597 0.16231 1.947 0.0538 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 0.380927)
Null deviance: 49.041 on 129 degrees of freedom
Residual deviance: 46.650 on 126 degrees of freedom
(148 Beobachtungen als fehlend gelöscht)
AIC: NA
Number of Fisher Scoring iterations: 4Anova(mod6.1,type=3) #If the interactions are not significant, type II gives a more powerful test.Analysis of Deviance Table (Type III tests)
Response: f_cMS
LR Chisq Df Pr(>Chisq)
Gr_size 0.0823 1 0.77417
Area 3.6353 1 0.05657 .
Gr_size:Area 3.8259 1 0.05047 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(mod6.1,type=2)Analysis of Deviance Table (Type II tests)
Response: f_cMS
LR Chisq Df Pr(>Chisq)
Gr_size 2.0658 1 0.15064
Area 0.6638 1 0.41523
Gr_size:Area 3.8259 1 0.05047 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Effect of density on number of mating partners in males.
mod5.1=glm(m_cMS~Gr_size*Area,data=DB_data,family = quasipoisson)
summary(mod5.1)
Call:
glm(formula = m_cMS ~ Gr_size * Area, family = quasipoisson,
data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.72066 -0.65999 0.09259 0.21801 1.91666
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.59471 0.08109 7.334 1.49e-11 ***
Gr_sizeLG 0.01555 0.10622 0.146 0.884
AreaSmall -0.05978 0.10965 -0.545 0.586
Gr_sizeLG:AreaSmall 0.09307 0.15229 0.611 0.542
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 0.3813407)
Null deviance: 52.151 on 147 degrees of freedom
Residual deviance: 51.738 on 144 degrees of freedom
(130 Beobachtungen als fehlend gelöscht)
AIC: NA
Number of Fisher Scoring iterations: 4Anova(mod5.1,type=3) #If the interactions are not significant, type II gives a more powerful test.Analysis of Deviance Table (Type III tests)
Response: m_cMS
LR Chisq Df Pr(>Chisq)
Gr_size 0.02146 1 0.8835
Area 0.29669 1 0.5860
Gr_size:Area 0.37275 1 0.5415Anova(mod5.1,type=2)Analysis of Deviance Table (Type II tests)
Response: m_cMS
LR Chisq Df Pr(>Chisq)
Gr_size 0.63583 1 0.4252
Area 0.02296 1 0.8796
Gr_size:Area 0.37275 1 0.5415Mating duration
p4<-ggplot(DB_data, aes(x=Sex, y=as.numeric(MatingDuration_av),fill=Treatment, col=Treatment)) +
geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=0.9),shape=19, alpha=0.75, size = 2)+
stat_summary(fun.min = function(z) { quantile(z,0.25) },
fun.max = function(z) { quantile(z,0.75) },
fun = mean,position=position_dodge(.9), size = 0.9,col='black',show.legend = F)+
scale_color_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))+
scale_fill_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))+
xlab('Sex')+ylab("Mean mating duration")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,390)+labs(tag = "")+
annotate("text",label='n =',x=0.5,y=390,size=4)+
annotate("text",label='25',x=.65,y=390,size=4)+
annotate("text",label='50',x=.88,y=390,size=4)+
annotate("text",label='29',x=1.11,y=390,size=4)+
annotate("text",label='34',x=1.34,y=390,size=4)+
annotate("text",label='45',x=1.65,y=390,size=4)+
annotate("text",label='35',x=1.88,y=390,size=4)+
annotate("text",label='32',x=2.11,y=390,size=4)+
annotate("text",label='45',x=2.34,y=390,size=4)+
theme(panel.border = element_blank(),
plot.margin = margin(0,2,0.1,0.2,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(1, 0.8),
plot.tag.position=c(0.01,0.98),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
guides(colour = guide_legend(override.aes = list(size=4)))
p4
Figure 3: Effects of density treatments on the Mating duration (in
seconds) of female and male focals. Black bars indicate means and
quartile borders.
Statistical models: Mating duration (Gaussian GLM)
Effect
of density on mating duration in females.
mod8.1=glm(f_MatingDuration_av~Gr_size*Area,data=DB_data,family = gaussian)
summary(mod8.1)
Call:
glm(formula = f_MatingDuration_av ~ Gr_size * Area, family = gaussian,
data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-43.228 -19.983 -5.731 13.658 257.772
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 80.228 5.125 15.655 <2e-16 ***
Gr_sizeLG -13.982 8.814 -1.586 0.115
AreaSmall -6.548 8.129 -0.805 0.422
Gr_sizeLG:AreaSmall 12.448 12.712 0.979 0.329
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 1234.32)
Null deviance: 158874 on 129 degrees of freedom
Residual deviance: 155524 on 126 degrees of freedom
(148 Beobachtungen als fehlend gelöscht)
AIC: 1300.2
Number of Fisher Scoring iterations: 2Anova(mod8.1,type=3) #If the interactions are not significant, type II gives a more powerful test.Analysis of Deviance Table (Type III tests)
Response: f_MatingDuration_av
LR Chisq Df Pr(>Chisq)
Gr_size 2.51631 1 0.1127
Area 0.64880 1 0.4205
Gr_size:Area 0.95889 1 0.3275Anova(mod8.1,type=2)Analysis of Deviance Table (Type II tests)
Response: f_MatingDuration_av
LR Chisq Df Pr(>Chisq)
Gr_size 1.58548 1 0.2080
Area 0.05438 1 0.8156
Gr_size:Area 0.95889 1 0.3275
Effect of density on mating duration in males.
mod7.1=glm(m_MatingDuration_av~Gr_size*Area,data=DB_data,family = gaussian)
summary(mod7.1)
Call:
glm(formula = m_MatingDuration_av ~ Gr_size * Area, family = gaussian,
data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-71.249 -20.238 -11.011 9.588 290.421
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 76.034 6.952 10.937 <2e-16 ***
Gr_sizeLG -5.046 9.136 -0.552 0.582
AreaSmall 5.640 9.276 0.608 0.544
Gr_sizeLG:AreaSmall 3.951 13.080 0.302 0.763
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 1546.487)
Null deviance: 225607 on 147 degrees of freedom
Residual deviance: 222694 on 144 degrees of freedom
(130 Beobachtungen als fehlend gelöscht)
AIC: 1512.8
Number of Fisher Scoring iterations: 2Anova(mod7.1,type=3) #If the interactions are not significant, type II gives a more powerful test.Analysis of Deviance Table (Type III tests)
Response: m_MatingDuration_av
LR Chisq Df Pr(>Chisq)
Gr_size 0.30506 1 0.5807
Area 0.36970 1 0.5432
Gr_size:Area 0.09125 1 0.7626Anova(mod7.1,type=2)Analysis of Deviance Table (Type II tests)
Response: m_MatingDuration_av
LR Chisq Df Pr(>Chisq)
Gr_size 0.22750 1 0.6334
Area 1.36027 1 0.2435
Gr_size:Area 0.09125 1 0.7626Mating encounters
p6<-ggplot(DB_data, aes(x=Sex, y=as.numeric(Total_Encounters),fill=Treatment, col=Treatment)) +
geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=0.9),shape=19, alpha=0.75, size = 2)+
stat_summary(fun.min = function(z) { quantile(z,0.25) },
fun.max = function(z) { quantile(z,0.75) },
fun = mean,position=position_dodge(.9), size = 0.9,col='black',show.legend = F)+
scale_color_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))+
scale_fill_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))+
xlab('Sex')+ylab("Mating encounters")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,33)+labs(tag = "")+
annotate("text",label='n =',x=0.5,y=33,size=4)+
annotate("text",label='38',x=.65,y=33,size=4)+
annotate("text",label='40',x=.88,y=33,size=4)+
annotate("text",label='53',x=1.11,y=33,size=4)+
annotate("text",label='33',x=1.34,y=33,size=4)+
annotate("text",label='47',x=1.65,y=33,size=4)+
annotate("text",label='35',x=1.88,y=33,size=4)+
annotate("text",label='38',x=2.11,y=33,size=4)+
annotate("text",label='50',x=2.34,y=33,size=4)+
theme(panel.border = element_blank(),
plot.margin = margin(0,2,0.1,0.2,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(1, 0.8),
plot.tag.position=c(0.01,0.98),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
guides(colour = guide_legend(override.aes = list(size=4)))
p6
Figure 4: Effects of density treatments on the number of mating
encounters (mating number + mating attempts) of female and male focals.
Black bars indicate means and quartile borders.
Statistical models: Mating encounters (Gaussian GLM)
Effect of density on mating encounters in females.
mod12.1=glm(f_Total_Encounters~Gr_size*Area,data=DB_data,family = gaussian)
summary(mod12.1)
Call:
glm(formula = f_Total_Encounters ~ Gr_size * Area, family = gaussian,
data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-8.2128 -2.8929 -0.8929 2.3871 15.3871
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.2128 0.6133 15.021 <2e-16 ***
Gr_sizeLG -2.2961 1.0549 -2.177 0.0314 *
AreaSmall 0.4001 0.9729 0.411 0.6816
Gr_sizeLG:AreaSmall -0.4239 1.5214 -0.279 0.7810
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 17.68047)
Null deviance: 2420.8 on 129 degrees of freedom
Residual deviance: 2227.7 on 126 degrees of freedom
(148 Beobachtungen als fehlend gelöscht)
AIC: 748.28
Number of Fisher Scoring iterations: 2Anova(mod12.1,type=3) #If the interactions are not significant, type II gives a more powerful test.Analysis of Deviance Table (Type III tests)
Response: f_Total_Encounters
LR Chisq Df Pr(>Chisq)
Gr_size 4.7374 1 0.02951 *
Area 0.1692 1 0.68086
Gr_size:Area 0.0776 1 0.78051
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(mod12.1,type=2)Analysis of Deviance Table (Type II tests)
Response: f_Total_Encounters
LR Chisq Df Pr(>Chisq)
Gr_size 10.8161 1 0.001006 **
Area 0.0919 1 0.761748
Gr_size:Area 0.0776 1 0.780509
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Effect of density on mating encounters in males.
mod11.1=glm(m_Total_Encounters~Gr_size*Area,data=DB_data,family = gaussian)
summary(mod11.1)
Call:
glm(formula = m_Total_Encounters ~ Gr_size * Area, family = gaussian,
data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-7.9512 -3.2727 -0.8304 1.8076 21.0488
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.43750 0.86564 12.058 < 2e-16 ***
Gr_sizeLG -3.16477 1.13767 -2.782 0.00613 **
AreaSmall 0.51372 1.15506 0.445 0.65716
Gr_sizeLG:AreaSmall -0.07677 1.62869 -0.047 0.96247
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 23.97842)
Null deviance: 3857.3 on 147 degrees of freedom
Residual deviance: 3452.9 on 144 degrees of freedom
(130 Beobachtungen als fehlend gelöscht)
AIC: 896.17
Number of Fisher Scoring iterations: 2Anova(mod11.1,type=3) #If the interactions are not significant, type II gives a more powerful test.Analysis of Deviance Table (Type III tests)
Response: m_Total_Encounters
LR Chisq Df Pr(>Chisq)
Gr_size 7.7384 1 0.005406 **
Area 0.1978 1 0.656496
Gr_size:Area 0.0022 1 0.962405
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(mod11.1,type=2)Analysis of Deviance Table (Type II tests)
Response: m_Total_Encounters
LR Chisq Df Pr(>Chisq)
Gr_size 15.4719 1 8.374e-05 ***
Area 0.3404 1 0.5596
Gr_size:Area 0.0022 1 0.9624
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Proportion of successful matings
p5<-ggplot(DB_data, aes(x=Sex, y=as.numeric(Prop_MS),fill=Treatment, col=Treatment)) +
geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=0.9),shape=19, alpha=0.75, size = 2)+
stat_summary(fun.min = function(z) { quantile(z,0.25) },
fun.max = function(z) { quantile(z,0.75) },
fun = mean,position=position_dodge(.9), size = 0.9,col='black',show.legend = F)+
scale_color_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))+
scale_fill_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))+
xlab('Sex')+ylab("Prop. of successful matings")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,1.1)+labs(tag = "")+
annotate("text",label='n =',x=0.5,y=1.1,size=4)+
annotate("text",label='33',x=.65,y=1.1,size=4)+
annotate("text",label='53',x=.88,y=1.1,size=4)+
annotate("text",label='41',x=1.11,y=1.1,size=4)+
annotate("text",label='38',x=1.34,y=1.1,size=4)+
annotate("text",label='50',x=1.65,y=1.1,size=4)+
annotate("text",label='38',x=1.88,y=1.1,size=4)+
annotate("text",label='35',x=2.11,y=1.1,size=4)+
annotate("text",label='47',x=2.34,y=1.1,size=4)+
theme(panel.border = element_blank(),
plot.margin = margin(0.1,2,0,0.2,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(1, 0.8),
plot.tag.position=c(0.01,0.98),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
guides(colour = guide_legend(override.aes = list(size=4)))
p5
Figure 5: Effects of density treatments on the proportion of successful
matings (mating number/mating number + mating attempts) of female and
male focals. Black bars indicate means and quartile borders.
Statistical models: Proportion of successful matings
(quasi-binomial GLM)
Effect of density on proportion of successful
matings in females.
mod10.1=glm(cbind(f_TotMatings,f_Attempts_number)~Gr_size*Area,data=DB_data,family = quasibinomial)
summary(mod10.1)
Call:
glm(formula = cbind(f_TotMatings, f_Attempts_number) ~ Gr_size *
Area, family = quasibinomial, data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.65128 -0.86141 -0.02239 0.78484 3.12720
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.5047 0.1167 -4.324 3.09e-05 ***
Gr_sizeLG -0.1170 0.2243 -0.522 0.6028
AreaSmall -0.3813 0.1900 -2.007 0.0469 *
Gr_sizeLG:AreaSmall 0.5059 0.3214 1.574 0.1180
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasibinomial family taken to be 1.384682)
Null deviance: 190.01 on 129 degrees of freedom
Residual deviance: 183.48 on 126 degrees of freedom
(148 Beobachtungen als fehlend gelöscht)
AIC: NA
Number of Fisher Scoring iterations: 4Anova(mod10.1,type=3) #If the interactions are not significant, type II gives a more powerful test.Analysis of Deviance Table (Type III tests)
Response: cbind(f_TotMatings, f_Attempts_number)
LR Chisq Df Pr(>Chisq)
Gr_size 0.2736 1 0.60095
Area 4.0830 1 0.04332 *
Gr_size:Area 2.4886 1 0.11467
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(mod10.1,type=2)Analysis of Deviance Table (Type II tests)
Response: cbind(f_TotMatings, f_Attempts_number)
LR Chisq Df Pr(>Chisq)
Gr_size 0.63128 1 0.4269
Area 1.82596 1 0.1766
Gr_size:Area 2.48862 1 0.1147
Effect of density on proportion of successful matings in
males.
mod9.1=glm(cbind(m_TotMatings,m_Attempts_number)~Gr_size*Area,data=DB_data,family = quasibinomial)
summary(mod9.1)
Call:
glm(formula = cbind(m_TotMatings, m_Attempts_number) ~ Gr_size *
Area, family = quasibinomial, data = DB_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5584 -0.6477 0.0929 0.6904 4.1909
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.80769 0.13579 -5.948 1.97e-08 ***
Gr_sizeLG 0.03374 0.19350 0.174 0.862
AreaSmall -0.06841 0.18037 -0.379 0.705
Gr_sizeLG:AreaSmall 0.04048 0.27794 0.146 0.884
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasibinomial family taken to be 1.313552)
Null deviance: 194.77 on 147 degrees of freedom
Residual deviance: 194.30 on 144 degrees of freedom
(130 Beobachtungen als fehlend gelöscht)
AIC: NA
Number of Fisher Scoring iterations: 4Anova(mod9.1,type=3) #If the interactions are not significant, type II gives a more powerful test.Analysis of Deviance Table (Type III tests)
Response: cbind(m_TotMatings, m_Attempts_number)
LR Chisq Df Pr(>Chisq)
Gr_size 0.030398 1 0.8616
Area 0.143691 1 0.7046
Gr_size:Area 0.021206 1 0.8842Anova(mod9.1,type=2)Analysis of Deviance Table (Type II tests)
Response: cbind(m_TotMatings, m_Attempts_number)
LR Chisq Df Pr(>Chisq)
Gr_size 0.147195 1 0.7012
Area 0.139939 1 0.7083
Gr_size:Area 0.021206 1 0.8842Reproductive success
Secondly, we tested the effect that the densities had on the
reproductive success of focal beetles.
p1<-ggplot(DB_data_clean, aes(x=Sex, y=as.numeric(Total_N_MTP1),fill=Treatment, col=Treatment)) +
geom_point(position=position_jitterdodge(jitter.width=0.5,jitter.height = 0,dodge.width=0.9),shape=19, alpha=0.75, size = 2)+
stat_summary(fun.min = function(z) { quantile(z,0.25) },
fun.max = function(z) { quantile(z,0.75) },
fun = mean,position=position_dodge(.9), size = 0.9,col='black',show.legend = F)+
scale_color_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))+
scale_fill_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))+
xlab('Sex')+ylab("Number of offspring")+ggtitle('')+ theme(plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels = c('Female','Male'),drop=FALSE)+ ylim(0,320)+labs(tag = "")+
annotate("text",label='n =',x=0.5,y=320,size=4)+
annotate("text",label='21',x=.65,y=320,size=4)+
annotate("text",label='35',x=.88,y=320,size=4)+
annotate("text",label='27',x=1.11,y=320,size=4)+
annotate("text",label='24',x=1.34,y=320,size=4)+
annotate("text",label='35',x=1.65,y=320,size=4)+
annotate("text",label='22',x=1.88,y=320,size=4)+
annotate("text",label='24',x=2.11,y=320,size=4)+
annotate("text",label='29',x=2.34,y=320,size=4)+
theme(panel.border = element_blank(),
plot.margin = margin(0,2,0,0.2,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(1, 0.8),
plot.tag.position=c(0.01,0.98),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
guides(colour = guide_legend(override.aes = list(size=4)))
p1
Figure 6: Effects of density treatments on the reproductive success of
female and male focals. Black bars indicate means and quartile
borders.
Statistical models: Reproductive success (quasi-Poisson
GLM)
Effect of denstiy on reproductive success in females.
mod1.1=glm(m_RS~Gr_size*Area,data=DB_data_clean,family = quasipoisson)
summary(mod1.1)
Call:
glm(formula = m_RS ~ Gr_size * Area, family = quasipoisson, data = DB_data_clean)
Deviance Residuals:
Min 1Q Median 3Q Max
-12.3806 -8.0414 -0.2133 3.8997 19.7825
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.053715 0.213468 18.990 <2e-16 ***
Gr_sizeLG -0.002772 0.268516 -0.010 0.992
AreaSmall 0.285404 0.265012 1.077 0.284
Gr_sizeLG:AreaSmall -0.349216 0.374965 -0.931 0.354
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 47.25457)
Null deviance: 5078.4 on 93 degrees of freedom
Residual deviance: 4959.6 on 90 degrees of freedom
(83 Beobachtungen als fehlend gelöscht)
AIC: NA
Number of Fisher Scoring iterations: 5Anova(mod1.1,type=3) #If the interactions are not significant, type II gives a more powerful test.Analysis of Deviance Table (Type III tests)
Response: m_RS
LR Chisq Df Pr(>Chisq)
Gr_size 0.00011 1 0.9918
Area 1.18954 1 0.2754
Gr_size:Area 0.88019 1 0.3481Anova(mod1.1,type=2)Analysis of Deviance Table (Type II tests)
Response: m_RS
LR Chisq Df Pr(>Chisq)
Gr_size 0.98030 1 0.3221
Area 0.36751 1 0.5444
Gr_size:Area 0.88019 1 0.3481
Effect of density on reproductive success in males.
mod2.1=glm(f_RS~Gr_size*Area,data=DB_data_clean,family = quasipoisson)
summary(mod2.1)
Call:
glm(formula = f_RS ~ Gr_size * Area, family = quasipoisson, data = DB_data_clean)
Deviance Residuals:
Min 1Q Median 3Q Max
-11.926 -10.110 2.150 4.641 8.376
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.1271 0.1469 28.090 <2e-16 ***
Gr_sizeLG -0.1771 0.2644 -0.670 0.505
AreaSmall -0.2407 0.2548 -0.945 0.348
Gr_sizeLG:AreaSmall 0.5549 0.3803 1.459 0.148
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 40.15118)
Null deviance: 4614.9 on 82 degrees of freedom
Residual deviance: 4518.3 on 79 degrees of freedom
(94 Beobachtungen als fehlend gelöscht)
AIC: NA
Number of Fisher Scoring iterations: 5Anova(mod2.1,type=3) #If the interactions are not significant, type II gives a more powerful test.Analysis of Deviance Table (Type III tests)
Response: f_RS
LR Chisq Df Pr(>Chisq)
Gr_size 0.45842 1 0.4984
Area 0.91451 1 0.3389
Gr_size:Area 2.17502 1 0.1403Anova(mod2.1,type=2)Analysis of Deviance Table (Type II tests)
Response: f_RS
LR Chisq Df Pr(>Chisq)
Gr_size 0.22121 1 0.6381
Area 0.00138 1 0.9703
Gr_size:Area 2.17502 1 0.1403Metrics of sexual selection
In this part of our analysis we estimated standardized metrics of
(sexual) selection.
Metrics:
- Opportunity for selection
- Opportunity for sexual selection
- Bateman gradient
- Jones
index
We used bootstrapping (10.000 bootstrap replicates) to
obtain 95% confidence intervals and permutation tests (10.000
permutations) to statistically compare treatments and sexes.
#I on prop offspring
#D = 0.26
#Male
DB_data_clean_0.26_rel_m_RS <-as.data.table(DB_data_clean_0.26$rel_m_RS)
c <- function(d, i){
d2 <- d[i,]
return(var(d2[,1], na.rm=TRUE))
}
I_0.26_Male_relRS_bootvar <- boot(DB_data_clean_0.26_rel_m_RS, c, R=10000)
#Female
DB_data_clean_0.26_rel_f_RS<-as.data.table(DB_data_clean_0.26$rel_f_RS)
I_0.26_Female_relRS_bootvar <- boot(DB_data_clean_0.26_rel_f_RS, c, R=10000)
#D = 0.52
#Male
DB_data_clean_0.52_rel_m_RS <-as.data.table(DB_data_clean_0.52$rel_m_RS)
I_0.52_Male_relRS_bootvar <- boot(DB_data_clean_0.52_rel_m_RS, c, R=10000)
#Female
DB_data_clean_0.52_rel_f_RS <-as.data.table(DB_data_clean_0.52$rel_f_RS)
I_0.52_Female_relRS_bootvar <- boot(DB_data_clean_0.52_rel_f_RS, c, R=10000)
#D = 0.67
#Male
DB_data_clean_0.67_rel_m_RS <-as.data.table(DB_data_clean_0.67$rel_m_RS)
I_0.67_Male_relRS_bootvar <- boot(DB_data_clean_0.67_rel_m_RS, c, R=10000)
#Female
DB_data_clean_0.67_rel_f_RS <-as.data.table(DB_data_clean_0.67$rel_f_RS)
I_0.67_Female_relRS_bootvar <- boot(DB_data_clean_0.67_rel_f_RS, c, R=10000)
#D = 1.33
#Male
DB_data_clean_1.33_rel_m_RS <-as.data.table(DB_data_clean_1.33$rel_m_RS)
I_1.33_Male_relRS_bootvar <- boot(DB_data_clean_1.33_rel_m_RS, c, R=10000)
#Female
DB_data_clean_1.33_rel_f_RS <-as.data.table(DB_data_clean_1.33$rel_f_RS)
I_1.33_Female_relRS_bootvar <- boot(DB_data_clean_1.33_rel_f_RS, c, R=10000)
rm("c")
# The opportunity for sexual selection ####
# Is=variance in relative mating success
#Is on number of mating partners
#D = 0.26
#Male
DB_data_clean_0.26_rel_m_cMS <-as.data.table(DB_data_clean_0.26$rel_m_cMS)
c <- function(d, i){
d2 <- d[i,]
return(var(d2[,1], na.rm=TRUE))
}
Is_0.26_Male_relMS_bootvar <- boot(DB_data_clean_0.26_rel_m_cMS, c, R=10000)
#Female
DB_data_clean_0.26_rel_f_cMS <-as.data.table(DB_data_clean_0.26$rel_f_cMS)
Is_0.26_Female_relMS_bootvar <- boot(DB_data_clean_0.26_rel_f_cMS, c, R=10000)
#D = 0.52
#Male
DB_data_clean_0.52_rel_m_cMS <-as.data.table(DB_data_clean_0.52$rel_m_cMS)
Is_0.52_Male_relMS_bootvar <- boot(DB_data_clean_0.52_rel_m_cMS, c, R=10000)
#Female
DB_data_clean_0.52_rel_f_cMS <-as.data.table(DB_data_clean_0.52$rel_f_cMS)
Is_0.52_Female_relMS_bootvar <- boot(DB_data_clean_0.52_rel_f_cMS, c, R=10000)
#D = 0.67
#Male
DB_data_clean_0.67_rel_m_cMS <-as.data.table(DB_data_clean_0.67$rel_m_cMS)
Is_0.67_Male_relMS_bootvar <- boot(DB_data_clean_0.67_rel_m_cMS, c, R=10000)
#Female
DB_data_clean_0.67_rel_f_cMS <-as.data.table(DB_data_clean_0.67$rel_f_cMS)
Is_0.67_Female_relMS_bootvar <- boot(DB_data_clean_0.67_rel_f_cMS, c, R=10000)
#D = 1.33
#Male
DB_data_clean_1.33_rel_m_cMS <-as.data.table(DB_data_clean_1.33$rel_m_cMS)
Is_1.33_Male_relMS_bootvar <- boot(DB_data_clean_1.33_rel_m_cMS, c, R=10000)
#Female
DB_data_clean_1.33_rel_f_cMS <-as.data.table(DB_data_clean_1.33$rel_f_cMS)
Is_1.33_Female_relMS_bootvar <- boot(DB_data_clean_1.33_rel_f_cMS, c, R=10000)
rm("c")
#Bateman gradient ####
#B=slope of ordinary least squares regressions of relative reproductive success on relative mating success
#D = 0.26
#Male
DB_data_clean_0.26_Male_B <-as.data.table(cbind(DB_data_clean_0.26$rel_m_RS,DB_data_clean_0.26$rel_m_cMS))
names(DB_data_clean_0.26_Male_B)=cbind('V1','V2')
c <- function(d, i){
d2 <- d[i,]
return(lm(V1 ~V2,data=d2)$coefficients[2])
}
B_0.26_Male_relMS_bootvar <- boot(DB_data_clean_0.26_Male_B, c, R=10000)
#Female
DB_data_clean_0.26_Female_B <-as.data.table(cbind(DB_data_clean_0.26$rel_f_RS,DB_data_clean_0.26$rel_f_cMS))
names(DB_data_clean_0.26_Female_B)=cbind('V1','V2')
B_0.26_Female_relMS_bootvar <- boot(DB_data_clean_0.26_Female_B, c, R=10000)
#D = 0.52
#Male
DB_data_clean_0.52_Male_B <-as.data.table(cbind(DB_data_clean_0.52$rel_m_RS,DB_data_clean_0.52$rel_m_cMS))
names(DB_data_clean_0.52_Male_B)=cbind('V1','V2')
B_0.52_Male_relMS_bootvar <- boot(DB_data_clean_0.52_Male_B, c, R=10000)
#Female
DB_data_clean_0.52_Female_B <-as.data.table(cbind(DB_data_clean_0.52$rel_f_RS,DB_data_clean_0.52$rel_f_cMS))
names(DB_data_clean_0.52_Female_B)=cbind('V1','V2')
B_0.52_Female_relMS_bootvar <- boot(DB_data_clean_0.52_Female_B, c, R=10000)
#D = 0.67
#Male
DB_data_clean_0.67_Male_B <-as.data.table(cbind(DB_data_clean_0.67$rel_m_RS,DB_data_clean_0.67$rel_m_cMS))
names(DB_data_clean_0.67_Male_B)=cbind('V1','V2')
B_0.67_Male_relMS_bootvar <- boot(DB_data_clean_0.67_Male_B, c, R=10000)
#Female
DB_data_clean_0.67_Female_B <-as.data.table(cbind(DB_data_clean_0.67$rel_f_RS,DB_data_clean_0.67$rel_f_cMS))
names(DB_data_clean_0.67_Female_B)=cbind('V1','V2')
B_0.67_Female_relMS_bootvar <- boot(DB_data_clean_0.67_Female_B, c, R=10000)
#D = 1.33
#Male
DB_data_clean_1.33_Male_B <-as.data.table(cbind(DB_data_clean_1.33$rel_m_RS,DB_data_clean_1.33$rel_m_cMS))
names(DB_data_clean_1.33_Male_B)=cbind('V1','V2')
B_1.33_Male_relMS_bootvar <- boot(DB_data_clean_1.33_Male_B, c, R=10000)
#Female
DB_data_clean_1.33_Female_B <-as.data.table(cbind(DB_data_clean_1.33$rel_f_RS,DB_data_clean_1.33$rel_f_cMS))
names(DB_data_clean_1.33_Female_B)=cbind('V1','V2')
B_1.33_Female_relMS_bootvar <- boot(DB_data_clean_1.33_Female_B, c, R=10000)
rm("c")
#Jones index ####
#S= Product of B and the square root of Is, which provides an upper limit of the strength of precopulatory sexual selection
#D = 0.26
#Male
c <- function(d, i){
d2 <- d[i,]
return(lm(d2$V1 ~d2$V2)$coefficients[2]*sqrt(var(d2$V2, na.rm=TRUE)))
}
S_0.26_Male_relMS_bootvar <- boot(DB_data_clean_0.26_Male_B, c, R=10000)
#Female
S_0.26_Female_relMS_bootvar <- boot(DB_data_clean_0.26_Female_B, c, R=10000)
#D = 0.52
#Male
S_0.52_Male_relMS_bootvar <- boot(DB_data_clean_0.52_Male_B, c, R=10000)
#Female
S_0.52_Female_relMS_bootvar <- boot(DB_data_clean_0.52_Female_B, c, R=10000)
#D = 0.67
#Male
S_0.67_Male_relMS_bootvar <- boot(DB_data_clean_0.67_Male_B, c, R=10000)
#Female
S_0.67_Female_relMS_bootvar <- boot(DB_data_clean_0.67_Female_B, c, R=10000)
#D = 1.33
#Male
S_1.33_Male_relMS_bootvar <- boot(DB_data_clean_1.33_Male_B, c, R=10000)
#Female
S_1.33_Female_relMS_bootvar <- boot(DB_data_clean_1.33_Female_B, c, R=10000)
rm("c")
#Save data table ####
PhenVarBoot_Table_Male_0.26_I <- as.data.frame(cbind("Male", "0.26", "Opportunity for selection", as.numeric(mean(I_0.26_Male_relRS_bootvar$t)), quantile(I_0.26_Male_relRS_bootvar$t,.025, names = FALSE), quantile(I_0.26_Male_relRS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.52_I <- as.data.frame(cbind("Male", "0.52", "Opportunity for selection", mean(I_0.52_Male_relRS_bootvar$t), quantile(I_0.52_Male_relRS_bootvar$t,.025, names = FALSE), quantile(I_0.52_Male_relRS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.67_I <- as.data.frame(cbind("Male", "0.67", "Opportunity for selection", mean(I_0.67_Male_relRS_bootvar$t), quantile(I_0.67_Male_relRS_bootvar$t,.025, names = FALSE), quantile(I_0.67_Male_relRS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_1.33_I <- as.data.frame(cbind("Male", "1.33", "Opportunity for selection", mean(I_1.33_Male_relRS_bootvar$t), quantile(I_1.33_Male_relRS_bootvar$t,.025, names = FALSE), quantile(I_1.33_Male_relRS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.26_Is <- as.data.frame(cbind("Male", "0.26", "Opportunity for sexual selection", mean(Is_0.26_Male_relMS_bootvar$t), quantile(Is_0.26_Male_relMS_bootvar$t,.025, names = FALSE), quantile(Is_0.26_Male_relMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.52_Is <- as.data.frame(cbind("Male", "0.52", "Opportunity for sexual selection", mean(Is_0.52_Male_relMS_bootvar$t), quantile(Is_0.52_Male_relMS_bootvar$t,.025, names = FALSE), quantile(Is_0.52_Male_relMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.67_Is <- as.data.frame(cbind("Male", "0.67", "Opportunity for sexual selection", mean(Is_0.67_Male_relMS_bootvar$t), quantile(Is_0.67_Male_relMS_bootvar$t,.025, names = FALSE), quantile(Is_0.67_Male_relMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_1.33_Is <- as.data.frame(cbind("Male", "1.33", "Opportunity for sexual selection", mean(Is_1.33_Male_relMS_bootvar$t), quantile(Is_1.33_Male_relMS_bootvar$t,.025, names = FALSE), quantile(Is_1.33_Male_relMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.26_B <- as.data.frame(cbind("Male", "0.26", "Bateman gradient", mean(B_0.26_Male_relMS_bootvar$t), quantile(B_0.26_Male_relMS_bootvar$t,.025, names = FALSE), quantile(B_0.26_Male_relMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.52_B <- as.data.frame(cbind("Male", "0.52", "Bateman gradient", mean(B_0.52_Male_relMS_bootvar$t), quantile(B_0.52_Male_relMS_bootvar$t,.025, names = FALSE), quantile(B_0.52_Male_relMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.67_B <- as.data.frame(cbind("Male", "0.67", "Bateman gradient", mean(B_0.67_Male_relMS_bootvar$t), quantile(B_0.67_Male_relMS_bootvar$t,.025, names = FALSE), quantile(B_0.67_Male_relMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_1.33_B <- as.data.frame(cbind("Male", "1.33", "Bateman gradient", mean(B_1.33_Male_relMS_bootvar$t), quantile(B_1.33_Male_relMS_bootvar$t,.025, names = FALSE), quantile(B_1.33_Male_relMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.26_S <- as.data.frame(cbind("Male", "0.26", "Maximum standardized sexual selection differential", mean(S_0.26_Male_relMS_bootvar$t,na.rm = T), quantile(S_0.26_Male_relMS_bootvar$t,.025, names = FALSE,na.rm = T), quantile(S_0.26_Male_relMS_bootvar$t,.975, names = FALSE,na.rm = T)))
PhenVarBoot_Table_Male_0.52_S <- as.data.frame(cbind("Male", "0.52", "Maximum standardized sexual selection differential", mean(S_0.52_Male_relMS_bootvar$t,na.rm = T), quantile(S_0.52_Male_relMS_bootvar$t,.025, names = FALSE,na.rm = T), quantile(S_0.52_Male_relMS_bootvar$t,.975, names = FALSE,na.rm = T)))
PhenVarBoot_Table_Male_0.67_S <- as.data.frame(cbind("Male", "0.67", "Maximum standardized sexual selection differential", mean(S_0.67_Male_relMS_bootvar$t,na.rm = T), quantile(S_0.67_Male_relMS_bootvar$t,.025, names = FALSE,na.rm = T), quantile(S_0.67_Male_relMS_bootvar$t,.975, names = FALSE,na.rm = T)))
PhenVarBoot_Table_Male_1.33_S <- as.data.frame(cbind("Male", "1.33", "Maximum standardized sexual selection differential", mean(S_1.33_Male_relMS_bootvar$t,na.rm = T), quantile(S_1.33_Male_relMS_bootvar$t,.025, names = FALSE,na.rm = T), quantile(S_1.33_Male_relMS_bootvar$t,.975, names = FALSE,na.rm = T)))
PhenVarBoot_Table_Female_0.26_I <- as.data.frame(cbind("Female", "0.26", "Opportunity for selection", mean(I_0.26_Female_relRS_bootvar$t), quantile(I_0.26_Female_relRS_bootvar$t,.025, names = FALSE), quantile(I_0.26_Female_relRS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_0.52_I <- as.data.frame(cbind("Female", "0.52", "Opportunity for selection", mean(I_0.52_Female_relRS_bootvar$t), quantile(I_0.52_Female_relRS_bootvar$t,.025, names = FALSE), quantile(I_0.52_Female_relRS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_0.67_I <- as.data.frame(cbind("Female", "0.67", "Opportunity for selection", mean(I_0.67_Female_relRS_bootvar$t), quantile(I_0.67_Female_relRS_bootvar$t,.025, names = FALSE), quantile(I_0.67_Female_relRS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_1.33_I <- as.data.frame(cbind("Female", "1.33", "Opportunity for selection", mean(I_1.33_Female_relRS_bootvar$t), quantile(I_1.33_Female_relRS_bootvar$t,.025, names = FALSE), quantile(I_1.33_Female_relRS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_0.26_Is <- as.data.frame(cbind("Female", "0.26", "Opportunity for sexual selection", mean(Is_0.26_Female_relMS_bootvar$t), quantile(Is_0.26_Female_relMS_bootvar$t,.025, names = FALSE), quantile(Is_0.26_Female_relMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_0.52_Is <- as.data.frame(cbind("Female", "0.52", "Opportunity for sexual selection", mean(Is_0.52_Female_relMS_bootvar$t), quantile(Is_0.52_Female_relMS_bootvar$t,.025, names = FALSE), quantile(Is_0.52_Female_relMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_0.67_Is <- as.data.frame(cbind("Female", "0.67", "Opportunity for sexual selection", mean(Is_0.67_Female_relMS_bootvar$t), quantile(Is_0.67_Female_relMS_bootvar$t,.025, names = FALSE), quantile(Is_0.67_Female_relMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_1.33_Is <- as.data.frame(cbind("Female", "1.33", "Opportunity for sexual selection", mean(Is_1.33_Female_relMS_bootvar$t), quantile(Is_1.33_Female_relMS_bootvar$t,.025, names = FALSE), quantile(Is_1.33_Female_relMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_0.26_B <- as.data.frame(cbind("Female", "0.26", "Bateman gradient", mean(B_0.26_Female_relMS_bootvar$t), quantile(B_0.26_Female_relMS_bootvar$t,.025, names = FALSE), quantile(B_0.26_Female_relMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_0.52_B <- as.data.frame(cbind("Female", "0.52", "Bateman gradient", mean(B_0.52_Female_relMS_bootvar$t), quantile(B_0.52_Female_relMS_bootvar$t,.025, names = FALSE), quantile(B_0.52_Female_relMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_0.67_B <- as.data.frame(cbind("Female", "0.67", "Bateman gradient", mean(B_0.67_Female_relMS_bootvar$t), quantile(B_0.67_Female_relMS_bootvar$t,.025, names = FALSE), quantile(B_0.67_Female_relMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_1.33_B <- as.data.frame(cbind("Female", "1.33", "Bateman gradient", mean(B_1.33_Female_relMS_bootvar$t), quantile(B_1.33_Female_relMS_bootvar$t,.025, names = FALSE), quantile(B_1.33_Female_relMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_0.26_S <- as.data.frame(cbind("Female", "0.26", "Maximum standardized sexual selection differential", mean(S_0.26_Female_relMS_bootvar$t,na.rm = T), quantile(S_0.26_Female_relMS_bootvar$t,.025, names = FALSE,na.rm = T), quantile(S_0.26_Female_relMS_bootvar$t,.975, names = FALSE,na.rm = T)))
PhenVarBoot_Table_Female_0.52_S <- as.data.frame(cbind("Female", "0.52", "Maximum standardized sexual selection differential", mean(S_0.52_Female_relMS_bootvar$t,na.rm = T), quantile(S_0.52_Female_relMS_bootvar$t,.025, names = FALSE,na.rm = T), quantile(S_0.52_Female_relMS_bootvar$t,.975, names = FALSE,na.rm = T)))
PhenVarBoot_Table_Female_0.67_S <- as.data.frame(cbind("Female", "0.67", "Maximum standardized sexual selection differential", mean(S_0.67_Female_relMS_bootvar$t,na.rm = T), quantile(S_0.67_Female_relMS_bootvar$t,.025, names = FALSE,na.rm = T), quantile(S_0.67_Female_relMS_bootvar$t,.975, names = FALSE,na.rm = T)))
PhenVarBoot_Table_Female_1.33_S <- as.data.frame(cbind("Female", "1.33", "Maximum standardized sexual selection differential", mean(S_1.33_Female_relMS_bootvar$t,na.rm = T), quantile(S_1.33_Female_relMS_bootvar$t,.025, names = FALSE,na.rm = T), quantile(S_1.33_Female_relMS_bootvar$t,.975, names = FALSE,na.rm = T)))
Table_BatemanMetrics <- as.data.frame(as.matrix(rbind(PhenVarBoot_Table_Male_0.26_I,PhenVarBoot_Table_Male_0.52_I,PhenVarBoot_Table_Male_0.67_I,PhenVarBoot_Table_Male_1.33_I,
PhenVarBoot_Table_Male_0.26_Is,PhenVarBoot_Table_Male_0.52_Is,PhenVarBoot_Table_Male_0.67_Is,PhenVarBoot_Table_Male_1.33_Is,
PhenVarBoot_Table_Male_0.26_B,PhenVarBoot_Table_Male_0.52_B,PhenVarBoot_Table_Male_0.67_B,PhenVarBoot_Table_Male_1.33_B,
PhenVarBoot_Table_Male_0.26_S,PhenVarBoot_Table_Male_0.52_S,PhenVarBoot_Table_Male_0.67_S,PhenVarBoot_Table_Male_1.33_S,
PhenVarBoot_Table_Female_0.26_I,PhenVarBoot_Table_Female_0.52_I,PhenVarBoot_Table_Female_0.67_I,PhenVarBoot_Table_Female_1.33_I,
PhenVarBoot_Table_Female_0.26_Is,PhenVarBoot_Table_Female_0.52_Is,PhenVarBoot_Table_Female_0.67_Is,PhenVarBoot_Table_Female_1.33_Is,
PhenVarBoot_Table_Female_0.26_B,PhenVarBoot_Table_Female_0.52_B,PhenVarBoot_Table_Female_0.67_B,PhenVarBoot_Table_Female_1.33_B,
PhenVarBoot_Table_Female_0.26_S,PhenVarBoot_Table_Female_0.52_S,PhenVarBoot_Table_Female_0.67_S,PhenVarBoot_Table_Female_1.33_S)),digits=3)
is.table(Table_BatemanMetrics)
colnames(Table_BatemanMetrics)[1] <- "Sex"
colnames(Table_BatemanMetrics)[2] <- "Treatment"
colnames(Table_BatemanMetrics)[3] <- "Variable"
colnames(Table_BatemanMetrics)[4] <- "Variance"
colnames(Table_BatemanMetrics)[5] <- "l95.CI"
colnames(Table_BatemanMetrics)[6] <- "u95.CI"
Table_BatemanMetrics[,4]=as.numeric(Table_BatemanMetrics[,4])
Table_BatemanMetrics[,5]=as.numeric(Table_BatemanMetrics[,5])
Table_BatemanMetrics[,6]=as.numeric(Table_BatemanMetrics[,6])
Table_BatemanMetrics_round=cbind(Table_BatemanMetrics[,c(1,2,3)],round(Table_BatemanMetrics[,c(4,5,6)],digit=3))
rownames(Table_BatemanMetrics_round) <- NULL#Bootstrap comparisons ####
#Treatment difference ####
#Males####
#I ####
#0.26vs0.52
Treat_diff_M_0.26vs0.52_I <- I_0.26_Male_relRS_bootvar$t - I_0.52_Male_relRS_bootvar$t
t_Treat_diff_M_0.26vs0.52_I=mean(Treat_diff_M_0.26vs0.52_I,na.rm=TRUE)
t_Treat_diff_M_0.26vs0.52_I_lower=quantile(Treat_diff_M_0.26vs0.52_I,.025,na.rm=TRUE)
t_Treat_diff_M_0.26vs0.52_I_upper=quantile(Treat_diff_M_0.26vs0.52_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.26$rel_m_RS, DB_data_clean_0.52$rel_m_RS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.26$rel_m_RS)) - var(na.omit(DB_data_clean_0.52$rel_m_RS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.26$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_0.52$rel_m_RS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.26vs0.52_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.26vs0.67
Treat_diff_M_0.26vs0.67_I <- I_0.26_Male_relRS_bootvar$t - I_0.67_Male_relRS_bootvar$t
t_Treat_diff_M_0.26vs0.67_I=mean(Treat_diff_M_0.26vs0.67_I,na.rm=TRUE)
t_Treat_diff_M_0.26vs0.67_I_lower=quantile(Treat_diff_M_0.26vs0.67_I,.025,na.rm=TRUE)
t_Treat_diff_M_0.26vs0.67_I_upper=quantile(Treat_diff_M_0.26vs0.67_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.26$rel_m_RS, DB_data_clean_0.67$rel_m_RS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.26$rel_m_RS)) - var(na.omit(DB_data_clean_0.67$rel_m_RS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.26$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_0.67$rel_m_RS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.26vs0.67_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.26vs1.33
Treat_diff_M_0.26vs1.33_I <- I_0.26_Male_relRS_bootvar$t - I_1.33_Male_relRS_bootvar$t
t_Treat_diff_M_0.26vs1.33_I=mean(Treat_diff_M_0.26vs1.33_I,na.rm=TRUE)
t_Treat_diff_M_0.26vs1.33_I_lower=quantile(Treat_diff_M_0.26vs1.33_I,.025,na.rm=TRUE)
t_Treat_diff_M_0.26vs1.33_I_upper=quantile(Treat_diff_M_0.26vs1.33_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.26$rel_m_RS, DB_data_clean_1.33$rel_m_RS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.26$rel_m_RS)) - var(na.omit(DB_data_clean_1.33$rel_m_RS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.26$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_1.33$rel_m_RS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.26vs1.33_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52vs0.67
Treat_diff_M_0.52vs0.67_I <- I_0.52_Male_relRS_bootvar$t - I_0.67_Male_relRS_bootvar$t
t_Treat_diff_M_0.52vs0.67_I=mean(Treat_diff_M_0.52vs0.67_I,na.rm=TRUE)
t_Treat_diff_M_0.52vs0.67_I_lower=quantile(Treat_diff_M_0.52vs0.67_I,.025,na.rm=TRUE)
t_Treat_diff_M_0.52vs0.67_I_upper=quantile(Treat_diff_M_0.52vs0.67_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.52$rel_m_RS, DB_data_clean_0.67$rel_m_RS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.52$rel_m_RS)) - var(na.omit(DB_data_clean_0.67$rel_m_RS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.52$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_0.67$rel_m_RS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.52vs0.67_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52vs1.33
Treat_diff_M_0.52vs1.33_I <- I_0.52_Male_relRS_bootvar$t - I_1.33_Male_relRS_bootvar$t
t_Treat_diff_M_0.52vs1.33_I=mean(Treat_diff_M_0.52vs1.33_I,na.rm=TRUE)
t_Treat_diff_M_0.52vs1.33_I_lower=quantile(Treat_diff_M_0.52vs1.33_I,.025,na.rm=TRUE)
t_Treat_diff_M_0.52vs1.33_I_upper=quantile(Treat_diff_M_0.52vs1.33_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.52$rel_m_RS, DB_data_clean_1.33$rel_m_RS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.52$rel_m_RS)) - var(na.omit(DB_data_clean_1.33$rel_m_RS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.52$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_1.33$rel_m_RS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.52vs1.33_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.67vs1.33
Treat_diff_M_0.67vs1.33_I <- I_0.67_Male_relRS_bootvar$t - I_1.33_Male_relRS_bootvar$t
t_Treat_diff_M_0.67vs1.33_I=mean(Treat_diff_M_0.67vs1.33_I,na.rm=TRUE)
t_Treat_diff_M_0.67vs1.33_I_lower=quantile(Treat_diff_M_0.67vs1.33_I,.025,na.rm=TRUE)
t_Treat_diff_M_0.67vs1.33_I_upper=quantile(Treat_diff_M_0.67vs1.33_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.67$rel_m_RS, DB_data_clean_1.33$rel_m_RS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.67$rel_m_RS)) - var(na.omit(DB_data_clean_1.33$rel_m_RS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.67$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_1.33$rel_m_RS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.67vs1.33_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Is ####
#Males
#0.26vs0.52
Treat_diff_M_0.26vs0.52_Is <- Is_0.26_Male_relMS_bootvar$t - Is_0.52_Male_relMS_bootvar$t
t_Treat_diff_M_0.26vs0.52_Is=mean(Treat_diff_M_0.26vs0.52_Is,na.rm=TRUE)
t_Treat_diff_M_0.26vs0.52_Is_lower=quantile(Treat_diff_M_0.26vs0.52_Is,.025,na.rm=TRUE)
t_Treat_diff_M_0.26vs0.52_Is_upper=quantile(Treat_diff_M_0.26vs0.52_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.26$rel_m_cMS, DB_data_clean_0.52$rel_m_cMS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.26$rel_m_cMS)) - var(na.omit(DB_data_clean_0.52$rel_m_cMS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.26$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_0.52$rel_m_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.26vs0.52_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.26vs0.67
Treat_diff_M_0.26vs0.67_Is <- Is_0.26_Male_relMS_bootvar$t - Is_0.67_Male_relMS_bootvar$t
t_Treat_diff_M_0.26vs0.67_Is=mean(Treat_diff_M_0.26vs0.67_Is,na.rm=TRUE)
t_Treat_diff_M_0.26vs0.67_Is_lower=quantile(Treat_diff_M_0.26vs0.67_Is,.025,na.rm=TRUE)
t_Treat_diff_M_0.26vs0.67_Is_upper=quantile(Treat_diff_M_0.26vs0.67_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.26$rel_m_cMS, DB_data_clean_0.67$rel_m_cMS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.26$rel_m_cMS)) - var(na.omit(DB_data_clean_0.67$rel_m_cMS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.26$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_0.67$rel_m_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.26vs0.67_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.26vs1.33
Treat_diff_M_0.26vs1.33_Is <- Is_0.26_Male_relMS_bootvar$t - Is_1.33_Male_relMS_bootvar$t
t_Treat_diff_M_0.26vs1.33_Is=mean(Treat_diff_M_0.26vs1.33_Is,na.rm=TRUE)
t_Treat_diff_M_0.26vs1.33_Is_lower=quantile(Treat_diff_M_0.26vs1.33_Is,.025,na.rm=TRUE)
t_Treat_diff_M_0.26vs1.33_Is_upper=quantile(Treat_diff_M_0.26vs1.33_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.26$rel_m_cMS, DB_data_clean_1.33$rel_m_cMS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.26$rel_m_cMS)) - var(na.omit(DB_data_clean_1.33$rel_m_cMS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.26$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_1.33$rel_m_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.26vs1.33_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52vs0.67
Treat_diff_M_0.52vs0.67_Is <- Is_0.52_Male_relMS_bootvar$t - Is_0.67_Male_relMS_bootvar$t
t_Treat_diff_M_0.52vs0.67_Is=mean(Treat_diff_M_0.52vs0.67_Is,na.rm=TRUE)
t_Treat_diff_M_0.52vs0.67_Is_lower=quantile(Treat_diff_M_0.52vs0.67_Is,.025,na.rm=TRUE)
t_Treat_diff_M_0.52vs0.67_Is_upper=quantile(Treat_diff_M_0.52vs0.67_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.52$rel_m_cMS, DB_data_clean_0.67$rel_m_cMS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.52$rel_m_cMS)) - var(na.omit(DB_data_clean_0.67$rel_m_cMS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.52$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_0.67$rel_m_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.52vs0.67_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52vs1.33
Treat_diff_M_0.52vs1.33_Is <- Is_0.52_Male_relMS_bootvar$t - Is_1.33_Male_relMS_bootvar$t
t_Treat_diff_M_0.52vs1.33_Is=mean(Treat_diff_M_0.52vs1.33_Is,na.rm=TRUE)
t_Treat_diff_M_0.52vs1.33_Is_lower=quantile(Treat_diff_M_0.52vs1.33_Is,.025,na.rm=TRUE)
t_Treat_diff_M_0.52vs1.33_Is_upper=quantile(Treat_diff_M_0.52vs1.33_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.52$rel_m_cMS, DB_data_clean_1.33$rel_m_cMS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.52$rel_m_cMS)) - var(na.omit( DB_data_clean_1.33$rel_m_cMS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.52$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data), length( DB_data_clean_1.33$rel_m_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.52vs1.33_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.67vs1.33
Treat_diff_M_0.67vs1.33_Is <- Is_0.67_Male_relMS_bootvar$t - Is_1.33_Male_relMS_bootvar$t
t_Treat_diff_M_0.67vs1.33_Is=mean(Treat_diff_M_0.67vs1.33_Is,na.rm=TRUE)
t_Treat_diff_M_0.67vs1.33_Is_lower=quantile(Treat_diff_M_0.67vs1.33_Is,.025,na.rm=TRUE)
t_Treat_diff_M_0.67vs1.33_Is_upper=quantile(Treat_diff_M_0.67vs1.33_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.67$rel_m_cMS, DB_data_clean_1.33$rel_m_cMS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.67$rel_m_cMS)) - var(na.omit( DB_data_clean_1.33$rel_m_cMS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length( DB_data_clean_0.67$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data), length( DB_data_clean_1.33$rel_m_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.67vs1.33_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#B ####
#Males
#0.26vs0.52
Treat_diff_M_0.26vs0.52_B <- B_0.26_Male_relMS_bootvar$t - B_0.52_Male_relMS_bootvar$t
t_Treat_diff_M_0.26vs0.52_B=mean(Treat_diff_M_0.26vs0.52_B,na.rm=TRUE)
t_Treat_diff_M_0.26vs0.52_B_lower=quantile(Treat_diff_M_0.26vs0.52_B,.025,na.rm=TRUE)
t_Treat_diff_M_0.26vs0.52_B_upper=quantile(Treat_diff_M_0.26vs0.52_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.26$rel_m_RS, DB_data_clean_0.52$rel_m_RS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.26$rel_m_cMS,DB_data_clean_0.52$rel_m_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.26$rel_m_RS ~DB_data_clean_0.26$rel_m_cMS)$coefficients[2] - lm(DB_data_clean_0.52$rel_m_RS ~DB_data_clean_0.52$rel_m_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.26$rel_m_RS),TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_0.52$rel_m_RS),TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.26$rel_m_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_0.52$rel_m_cMS),TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.26vs0.52_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.26vs0.67
Treat_diff_M_0.26vs0.67_B <- B_0.26_Male_relMS_bootvar$t - B_0.67_Male_relMS_bootvar$t
t_Treat_diff_M_0.26vs0.67_B=mean(Treat_diff_M_0.26vs0.67_B,na.rm=TRUE)
t_Treat_diff_M_0.26vs0.67_B_lower=quantile(Treat_diff_M_0.26vs0.67_B,.025,na.rm=TRUE)
t_Treat_diff_M_0.26vs0.67_B_upper=quantile(Treat_diff_M_0.26vs0.67_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.26$rel_m_RS, DB_data_clean_0.67$rel_m_RS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.26$rel_m_cMS,DB_data_clean_0.67$rel_m_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.26$rel_m_RS ~DB_data_clean_0.26$rel_m_cMS)$coefficients[2] - lm(DB_data_clean_0.67$rel_m_RS ~DB_data_clean_0.67$rel_m_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.26$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_0.67$rel_m_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.26$rel_m_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_0.67$rel_m_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.26vs0.67_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.26vs1.33
Treat_diff_M_0.26vs1.33_B <- B_0.26_Male_relMS_bootvar$t - B_1.33_Male_relMS_bootvar$t
t_Treat_diff_M_0.26vs1.33_B=mean(Treat_diff_M_0.26vs1.33_B,na.rm=TRUE)
t_Treat_diff_M_0.26vs1.33_B_lower=quantile(Treat_diff_M_0.26vs1.33_B,.025,na.rm=TRUE)
t_Treat_diff_M_0.26vs1.33_B_upper=quantile(Treat_diff_M_0.26vs1.33_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.26$rel_m_RS, DB_data_clean_1.33$rel_m_RS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.26$rel_m_cMS,DB_data_clean_1.33$rel_m_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.26$rel_m_RS ~DB_data_clean_0.26$rel_m_cMS)$coefficients[2] - lm(DB_data_clean_1.33$rel_m_RS ~DB_data_clean_1.33$rel_m_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.26$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_1.33$rel_m_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.26$rel_m_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_1.33$rel_m_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.26vs1.33_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52vs0.67
Treat_diff_M_0.52vs0.67_B <- B_0.52_Male_relMS_bootvar$t - B_0.67_Male_relMS_bootvar$t
t_Treat_diff_M_0.52vs0.67_B=mean(Treat_diff_M_0.52vs0.67_B,na.rm=TRUE)
t_Treat_diff_M_0.52vs0.67_B_lower=quantile(Treat_diff_M_0.52vs0.67_B,.025,na.rm=TRUE)
t_Treat_diff_M_0.52vs0.67_B_upper=quantile(Treat_diff_M_0.52vs0.67_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.52$rel_m_RS, DB_data_clean_0.67$rel_m_RS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.52$rel_m_cMS,DB_data_clean_0.67$rel_m_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.52$rel_m_RS ~DB_data_clean_0.52$rel_m_cMS)$coefficients[2] - lm(DB_data_clean_0.67$rel_m_RS ~DB_data_clean_0.67$rel_m_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.52$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_0.67$rel_m_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.52$rel_m_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_0.67$rel_m_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.52vs0.67_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52vs1.33
Treat_diff_M_0.52vs1.33_B <- B_0.52_Male_relMS_bootvar$t - B_1.33_Male_relMS_bootvar$t
t_Treat_diff_M_0.52vs1.33_B=mean(Treat_diff_M_0.52vs1.33_B,na.rm=TRUE)
t_Treat_diff_M_0.52vs1.33_B_lower=quantile(Treat_diff_M_0.52vs1.33_B,.025,na.rm=TRUE)
t_Treat_diff_M_0.52vs1.33_B_upper=quantile(Treat_diff_M_0.52vs1.33_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.52$rel_m_RS, DB_data_clean_1.33$rel_m_RS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.52$rel_m_cMS,DB_data_clean_1.33$rel_m_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.52$rel_m_RS ~DB_data_clean_0.52$rel_m_cMS)$coefficients[2] - lm(DB_data_clean_1.33$rel_m_RS ~DB_data_clean_1.33$rel_m_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.52$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_1.33$rel_m_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.52$rel_m_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_1.33$rel_m_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.52vs1.33_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.67vs1.33
Treat_diff_M_0.67vs1.33_B <- B_0.67_Male_relMS_bootvar$t - B_1.33_Male_relMS_bootvar$t
t_Treat_diff_M_0.67vs1.33_B=mean(Treat_diff_M_0.67vs1.33_B,na.rm=TRUE)
t_Treat_diff_M_0.67vs1.33_B_lower=quantile(Treat_diff_M_0.67vs1.33_B,.025,na.rm=TRUE)
t_Treat_diff_M_0.67vs1.33_B_upper=quantile(Treat_diff_M_0.67vs1.33_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.67$rel_m_RS, DB_data_clean_1.33$rel_m_RS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.67$rel_m_cMS,DB_data_clean_1.33$rel_m_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.67$rel_m_RS ~DB_data_clean_0.67$rel_m_cMS)$coefficients[2] - lm(DB_data_clean_1.33$rel_m_RS ~DB_data_clean_1.33$rel_m_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.67$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_1.33$rel_m_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.67$rel_m_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_1.33$rel_m_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.67vs1.33_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#S ####
#Males
#0.26vs0.52
Treat_diff_M_0.26vs0.52_S <- S_0.26_Male_relMS_bootvar$t - S_0.52_Male_relMS_bootvar$t
t_Treat_diff_M_0.26vs0.52_S=mean(Treat_diff_M_0.26vs0.52_S,na.rm=TRUE)
t_Treat_diff_M_0.26vs0.52_S_lower=quantile(Treat_diff_M_0.26vs0.52_S,.025,na.rm=TRUE)
t_Treat_diff_M_0.26vs0.52_S_upper=quantile(Treat_diff_M_0.26vs0.52_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.26$rel_m_cMS, DB_data_clean_0.52$rel_m_cMS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.26$rel_m_RS, DB_data_clean_0.52$rel_m_RS,recursive = T , use.names = F)
comb_data3=c(DB_data_clean_0.26$rel_m_cMS, DB_data_clean_0.52$rel_m_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.26$rel_m_RS ~DB_data_clean_0.26$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.26$rel_m_cMS, na.rm=TRUE)) - lm(DB_data_clean_0.52$rel_m_RS ~DB_data_clean_0.52$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.52$rel_m_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.26$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_0.52$rel_m_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.26$rel_m_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_0.52$rel_m_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.26vs0.52_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.26vs0.67
Treat_diff_M_0.26vs0.67_S <- S_0.26_Male_relMS_bootvar$t - S_0.67_Male_relMS_bootvar$t
t_Treat_diff_M_0.26vs0.67_S=mean(Treat_diff_M_0.26vs0.67_S,na.rm=TRUE)
t_Treat_diff_M_0.26vs0.67_S_lower=quantile(Treat_diff_M_0.26vs0.67_S,.025,na.rm=TRUE)
t_Treat_diff_M_0.26vs0.67_S_upper=quantile(Treat_diff_M_0.26vs0.67_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.26$rel_m_cMS, DB_data_clean_0.67$rel_m_cMS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.26$rel_m_RS, DB_data_clean_0.67$rel_m_RS,recursive = T , use.names = F)
comb_data3=c(DB_data_clean_0.26$rel_m_cMS, DB_data_clean_0.67$rel_m_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.26$rel_m_RS ~DB_data_clean_0.26$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.26$rel_m_cMS, na.rm=TRUE)) - lm(DB_data_clean_0.67$rel_m_RS ~DB_data_clean_0.67$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.67$rel_m_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.26$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_0.67$rel_m_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.26$rel_m_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_0.67$rel_m_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.26vs0.67_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.26vs1.33
Treat_diff_M_0.26vs1.33_S <- S_0.26_Male_relMS_bootvar$t - S_1.33_Male_relMS_bootvar$t
t_Treat_diff_M_0.26vs1.33_S=mean(Treat_diff_M_0.26vs1.33_S,na.rm=TRUE)
t_Treat_diff_M_0.26vs1.33_S_lower=quantile(Treat_diff_M_0.26vs1.33_S,.025,na.rm=TRUE)
t_Treat_diff_M_0.26vs1.33_S_upper=quantile(Treat_diff_M_0.26vs1.33_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.26$rel_m_cMS, DB_data_clean_1.33$rel_m_cMS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.26$rel_m_RS, DB_data_clean_1.33$rel_m_RS,recursive = T , use.names = F)
comb_data3=c(DB_data_clean_0.26$rel_m_cMS, DB_data_clean_1.33$rel_m_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.26$rel_m_RS ~DB_data_clean_0.26$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.26$rel_m_cMS, na.rm=TRUE)) - lm(DB_data_clean_1.33$rel_m_RS ~DB_data_clean_1.33$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_1.33$rel_m_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.26$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_1.33$rel_m_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.26$rel_m_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_1.33$rel_m_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.26vs1.33_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52vs0.67
Treat_diff_M_0.52vs0.67_S <- S_0.52_Male_relMS_bootvar$t - S_0.67_Male_relMS_bootvar$t
t_Treat_diff_M_0.52vs0.67_S=mean(Treat_diff_M_0.52vs0.67_S,na.rm=TRUE)
t_Treat_diff_M_0.52vs0.67_S_lower=quantile(Treat_diff_M_0.52vs0.67_S,.025,na.rm=TRUE)
t_Treat_diff_M_0.52vs0.67_S_upper=quantile(Treat_diff_M_0.52vs0.67_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.52$rel_m_cMS, DB_data_clean_0.67$rel_m_cMS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.52$rel_m_RS, DB_data_clean_0.67$rel_m_RS,recursive = T , use.names = F)
comb_data3=c(DB_data_clean_0.52$rel_m_cMS, DB_data_clean_0.67$rel_m_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.52$rel_m_RS ~DB_data_clean_0.52$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.52$rel_m_cMS, na.rm=TRUE)) - lm(DB_data_clean_0.67$rel_m_RS ~DB_data_clean_0.67$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.67$rel_m_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.52$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_0.67$rel_m_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.52$rel_m_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_0.67$rel_m_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.52vs0.67_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52vs1.33
Treat_diff_M_0.52vs1.33_S <- S_0.52_Male_relMS_bootvar$t - S_1.33_Male_relMS_bootvar$t
t_Treat_diff_M_0.52vs1.33_S=mean(Treat_diff_M_0.52vs1.33_S,na.rm=TRUE)
t_Treat_diff_M_0.52vs1.33_S_lower=quantile(Treat_diff_M_0.52vs1.33_S,.025,na.rm=TRUE)
t_Treat_diff_M_0.52vs1.33_S_upper=quantile(Treat_diff_M_0.52vs1.33_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.52$rel_m_cMS, DB_data_clean_1.33$rel_m_cMS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.52$rel_m_RS, DB_data_clean_1.33$rel_m_RS,recursive = T , use.names = F)
comb_data3=c(DB_data_clean_0.52$rel_m_cMS, DB_data_clean_1.33$rel_m_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.52$rel_m_RS ~DB_data_clean_0.52$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.52$rel_m_cMS, na.rm=TRUE)) - lm(DB_data_clean_1.33$rel_m_RS ~DB_data_clean_1.33$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_1.33$rel_m_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.52$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_1.33$rel_m_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.52$rel_m_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_1.33$rel_m_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.52vs1.33_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.67vs1.33
Treat_diff_M_0.67vs1.33_S <- S_0.67_Male_relMS_bootvar$t - S_1.33_Male_relMS_bootvar$t
t_Treat_diff_M_0.67vs1.33_S=mean(Treat_diff_M_0.67vs1.33_S,na.rm=TRUE)
t_Treat_diff_M_0.67vs1.33_S_lower=quantile(Treat_diff_M_0.67vs1.33_S,.025,na.rm=TRUE)
t_Treat_diff_M_0.67vs1.33_S_upper=quantile(Treat_diff_M_0.67vs1.33_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.67$rel_m_cMS, DB_data_clean_1.33$rel_m_cMS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.67$rel_m_RS, DB_data_clean_1.33$rel_m_RS,recursive = T , use.names = F)
comb_data3=c(DB_data_clean_0.67$rel_m_cMS, DB_data_clean_1.33$rel_m_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.67$rel_m_RS ~DB_data_clean_0.67$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.67$rel_m_cMS, na.rm=TRUE)) - lm(DB_data_clean_1.33$rel_m_RS ~DB_data_clean_1.33$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_1.33$rel_m_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.67$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_1.33$rel_m_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.67$rel_m_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_1.33$rel_m_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_M_0.67vs1.33_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Females####
#I ####
#0.26vs0.52
Treat_diff_F_0.26vs0.52_I <- I_0.26_Female_relRS_bootvar$t - I_0.52_Female_relRS_bootvar$t
t_Treat_diff_F_0.26vs0.52_I=mean(Treat_diff_F_0.26vs0.52_I,na.rm=TRUE)
t_Treat_diff_F_0.26vs0.52_I_lower=quantile(Treat_diff_F_0.26vs0.52_I,.025,na.rm=TRUE)
t_Treat_diff_F_0.26vs0.52_I_upper=quantile(Treat_diff_F_0.26vs0.52_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.26$rel_f_RS, DB_data_clean_0.52$rel_f_RS)
diff.observed = var(na.omit(DB_data_clean_0.26$rel_f_RS)) - var(na.omit(DB_data_clean_0.52$rel_f_RS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.26$rel_f_RS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_0.52$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.26vs0.52_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.26vs0.67
Treat_diff_F_0.26vs0.67_I <- I_0.26_Female_relRS_bootvar$t - I_0.67_Female_relRS_bootvar$t
t_Treat_diff_F_0.26vs0.67_I=mean(Treat_diff_F_0.26vs0.67_I,na.rm=TRUE)
t_Treat_diff_F_0.26vs0.67_I_lower=quantile(Treat_diff_F_0.26vs0.67_I,.025,na.rm=TRUE)
t_Treat_diff_F_0.26vs0.67_I_upper=quantile(Treat_diff_F_0.26vs0.67_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.26$rel_f_RS, DB_data_clean_0.67$rel_f_RS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.26$rel_f_RS)) - var(na.omit(DB_data_clean_0.67$rel_f_RS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.26$rel_f_RS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_0.67$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.26vs0.67_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.26vs1.33
Treat_diff_F_0.26vs1.33_I <- I_0.26_Female_relRS_bootvar$t - I_1.33_Female_relRS_bootvar$t
t_Treat_diff_F_0.26vs1.33_I=mean(Treat_diff_F_0.26vs1.33_I,na.rm=TRUE)
t_Treat_diff_F_0.26vs1.33_I_lower=quantile(Treat_diff_F_0.26vs1.33_I,.025,na.rm=TRUE)
t_Treat_diff_F_0.26vs1.33_I_upper=quantile(Treat_diff_F_0.26vs1.33_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.26$rel_f_RS, DB_data_clean_1.33$rel_f_RS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.26$rel_f_RS)) - var(na.omit(DB_data_clean_1.33$rel_f_RS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.26$rel_f_RS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_1.33$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.26vs1.33_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52vs0.67
Treat_diff_F_0.52vs0.67_I <- I_0.52_Female_relRS_bootvar$t - I_0.67_Female_relRS_bootvar$t
t_Treat_diff_F_0.52vs0.67_I=mean(Treat_diff_F_0.52vs0.67_I,na.rm=TRUE)
t_Treat_diff_F_0.52vs0.67_I_lower=quantile(Treat_diff_F_0.52vs0.67_I,.025,na.rm=TRUE)
t_Treat_diff_F_0.52vs0.67_I_upper=quantile(Treat_diff_F_0.52vs0.67_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.52$rel_f_RS, DB_data_clean_0.67$rel_f_RS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.52$rel_f_RS)) - var(na.omit(DB_data_clean_0.67$rel_f_RS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.52$rel_f_RS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_0.67$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.52vs0.67_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52vs1.33
Treat_diff_F_0.52vs1.33_I <- I_0.52_Female_relRS_bootvar$t - I_1.33_Female_relRS_bootvar$t
t_Treat_diff_F_0.52vs1.33_I=mean(Treat_diff_F_0.52vs1.33_I,na.rm=TRUE)
t_Treat_diff_F_0.52vs1.33_I_lower=quantile(Treat_diff_F_0.52vs1.33_I,.025,na.rm=TRUE)
t_Treat_diff_F_0.52vs1.33_I_upper=quantile(Treat_diff_F_0.52vs1.33_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.52$rel_f_RS, DB_data_clean_1.33$rel_f_RS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.52$rel_f_RS)) - var(na.omit(DB_data_clean_1.33$rel_f_RS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.52$rel_f_RS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_1.33$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.52vs1.33_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.67vs1.33
Treat_diff_F_0.67vs1.33_I <- I_0.67_Female_relRS_bootvar$t - I_1.33_Female_relRS_bootvar$t
t_Treat_diff_F_0.67vs1.33_I=mean(Treat_diff_F_0.67vs1.33_I,na.rm=TRUE)
t_Treat_diff_F_0.67vs1.33_I_lower=quantile(Treat_diff_F_0.67vs1.33_I,.025,na.rm=TRUE)
t_Treat_diff_F_0.67vs1.33_I_upper=quantile(Treat_diff_F_0.67vs1.33_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.67$rel_f_RS, DB_data_clean_1.33$rel_f_RS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.67$rel_f_RS)) - var(na.omit(DB_data_clean_1.33$rel_f_RS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.67$rel_f_RS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_1.33$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.67vs1.33_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Is ####
#Females
#0.26vs0.52
Treat_diff_F_0.26vs0.52_Is <- Is_0.26_Female_relMS_bootvar$t - Is_0.52_Female_relMS_bootvar$t
t_Treat_diff_F_0.26vs0.52_Is=mean(Treat_diff_F_0.26vs0.52_Is,na.rm=TRUE)
t_Treat_diff_F_0.26vs0.52_Is_lower=quantile(Treat_diff_F_0.26vs0.52_Is,.025,na.rm=TRUE)
t_Treat_diff_F_0.26vs0.52_Is_upper=quantile(Treat_diff_F_0.26vs0.52_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.26$rel_f_cMS, DB_data_clean_0.52$rel_f_cMS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.26$rel_f_cMS)) - var(na.omit(DB_data_clean_0.52$rel_f_cMS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.26$rel_f_cMS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_0.52$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.26vs0.52_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.26vs0.67
Treat_diff_F_0.26vs0.67_Is <- Is_0.26_Female_relMS_bootvar$t - Is_0.67_Female_relMS_bootvar$t
t_Treat_diff_F_0.26vs0.67_Is=mean(Treat_diff_F_0.26vs0.67_Is,na.rm=TRUE)
t_Treat_diff_F_0.26vs0.67_Is_lower=quantile(Treat_diff_F_0.26vs0.67_Is,.025,na.rm=TRUE)
t_Treat_diff_F_0.26vs0.67_Is_upper=quantile(Treat_diff_F_0.26vs0.67_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.26$rel_f_cMS, DB_data_clean_0.67$rel_f_cMS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.26$rel_f_cMS)) - var(na.omit(DB_data_clean_0.67$rel_f_cMS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.26$rel_f_cMS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_0.67$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.26vs0.67_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.26vs1.33
Treat_diff_F_0.26vs1.33_Is <- Is_0.26_Female_relMS_bootvar$t - Is_1.33_Female_relMS_bootvar$t
t_Treat_diff_F_0.26vs1.33_Is=mean(Treat_diff_F_0.26vs1.33_Is,na.rm=TRUE)
t_Treat_diff_F_0.26vs1.33_Is_lower=quantile(Treat_diff_F_0.26vs1.33_Is,.025,na.rm=TRUE)
t_Treat_diff_F_0.26vs1.33_Is_upper=quantile(Treat_diff_F_0.26vs1.33_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.26$rel_f_cMS, DB_data_clean_1.33$rel_f_cMS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.26$rel_f_cMS)) - var(na.omit(DB_data_clean_1.33$rel_f_cMS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.26$rel_f_cMS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_1.33$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.26vs1.33_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52vs0.67
Treat_diff_F_0.52vs0.67_Is <- Is_0.52_Female_relMS_bootvar$t - Is_0.67_Female_relMS_bootvar$t
t_Treat_diff_F_0.52vs0.67_Is=mean(Treat_diff_F_0.52vs0.67_Is,na.rm=TRUE)
t_Treat_diff_F_0.52vs0.67_Is_lower=quantile(Treat_diff_F_0.52vs0.67_Is,.025,na.rm=TRUE)
t_Treat_diff_F_0.52vs0.67_Is_upper=quantile(Treat_diff_F_0.52vs0.67_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.52$rel_f_cMS, DB_data_clean_0.67$rel_f_cMS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.52$rel_f_cMS)) - var(na.omit(DB_data_clean_0.67$rel_f_cMS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.52$rel_f_cMS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_0.67$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.52vs0.67_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52vs1.33
Treat_diff_F_0.52vs1.33_Is <- Is_0.52_Female_relMS_bootvar$t - Is_1.33_Female_relMS_bootvar$t
t_Treat_diff_F_0.52vs1.33_Is=mean(Treat_diff_F_0.52vs1.33_Is,na.rm=TRUE)
t_Treat_diff_F_0.52vs1.33_Is_lower=quantile(Treat_diff_F_0.52vs1.33_Is,.025,na.rm=TRUE)
t_Treat_diff_F_0.52vs1.33_Is_upper=quantile(Treat_diff_F_0.52vs1.33_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.52$rel_f_cMS, DB_data_clean_1.33$rel_f_cMS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.52$rel_f_cMS)) - var(na.omit(DB_data_clean_1.33$rel_f_cMS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.52$rel_f_cMS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_1.33$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.52vs1.33_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.67vs1.33
Treat_diff_F_0.67vs1.33_Is <- Is_0.67_Female_relMS_bootvar$t - Is_1.33_Female_relMS_bootvar$t
t_Treat_diff_F_0.67vs1.33_Is=mean(Treat_diff_F_0.67vs1.33_Is,na.rm=TRUE)
t_Treat_diff_F_0.67vs1.33_Is_lower=quantile(Treat_diff_F_0.67vs1.33_Is,.025,na.rm=TRUE)
t_Treat_diff_F_0.67vs1.33_Is_upper=quantile(Treat_diff_F_0.67vs1.33_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.67$rel_f_cMS, DB_data_clean_1.33$rel_f_cMS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.67$rel_f_cMS)) - var(na.omit(DB_data_clean_1.33$rel_f_cMS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.67$rel_f_cMS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_1.33$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.67vs1.33_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#B ####
#Females
#0.26vs0.52
Treat_diff_F_0.26vs0.52_B <- B_0.26_Female_relMS_bootvar$t - B_0.52_Female_relMS_bootvar$t
t_Treat_diff_F_0.26vs0.52_B=mean(Treat_diff_F_0.26vs0.52_B,na.rm=TRUE)
t_Treat_diff_F_0.26vs0.52_B_lower=quantile(Treat_diff_F_0.26vs0.52_B,.025,na.rm=TRUE)
t_Treat_diff_F_0.26vs0.52_B_upper=quantile(Treat_diff_F_0.26vs0.52_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.26$rel_f_RS, DB_data_clean_0.52$rel_f_RS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.26$rel_f_cMS,DB_data_clean_0.52$rel_f_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.26$rel_f_RS ~DB_data_clean_0.26$rel_f_cMS)$coefficients[2] - lm(DB_data_clean_0.52$rel_f_RS ~DB_data_clean_0.52$rel_f_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.26$rel_f_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_0.52$rel_f_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.26$rel_f_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_0.52$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.26vs0.52_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.26vs0.67
Treat_diff_F_0.26vs0.67_B <- B_0.26_Female_relMS_bootvar$t - B_0.67_Female_relMS_bootvar$t
t_Treat_diff_F_0.26vs0.67_B=mean(Treat_diff_F_0.26vs0.67_B,na.rm=TRUE)
t_Treat_diff_F_0.26vs0.67_B_lower=quantile(Treat_diff_F_0.26vs0.67_B,.025,na.rm=TRUE)
t_Treat_diff_F_0.26vs0.67_B_upper=quantile(Treat_diff_F_0.26vs0.67_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.26$rel_f_RS, DB_data_clean_0.67$rel_f_RS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.26$rel_f_cMS,DB_data_clean_0.67$rel_f_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.26$rel_f_RS ~DB_data_clean_0.26$rel_f_cMS)$coefficients[2] - lm(DB_data_clean_0.67$rel_f_RS ~DB_data_clean_0.67$rel_f_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.26$rel_f_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_0.67$rel_f_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.26$rel_f_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_0.67$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.26vs0.67_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.26vs1.33
Treat_diff_F_0.26vs1.33_B <- B_0.26_Female_relMS_bootvar$t - B_1.33_Female_relMS_bootvar$t
t_Treat_diff_F_0.26vs1.33_B=mean(Treat_diff_F_0.26vs1.33_B,na.rm=TRUE)
t_Treat_diff_F_0.26vs1.33_B_lower=quantile(Treat_diff_F_0.26vs1.33_B,.025,na.rm=TRUE)
t_Treat_diff_F_0.26vs1.33_B_upper=quantile(Treat_diff_F_0.26vs1.33_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.26$rel_f_RS, DB_data_clean_1.33$rel_f_RS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.26$rel_f_cMS,DB_data_clean_1.33$rel_f_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.26$rel_f_RS ~DB_data_clean_0.26$rel_f_cMS)$coefficients[2] - lm(DB_data_clean_1.33$rel_f_RS ~DB_data_clean_1.33$rel_f_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.26$rel_f_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_1.33$rel_f_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.26$rel_f_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_1.33$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.26vs1.33_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52vs0.67
Treat_diff_F_0.52vs0.67_B <- B_0.52_Female_relMS_bootvar$t - B_0.67_Female_relMS_bootvar$t
t_Treat_diff_F_0.52vs0.67_B=mean(Treat_diff_F_0.52vs0.67_B,na.rm=TRUE)
t_Treat_diff_F_0.52vs0.67_B_lower=quantile(Treat_diff_F_0.52vs0.67_B,.025,na.rm=TRUE)
t_Treat_diff_F_0.52vs0.67_B_upper=quantile(Treat_diff_F_0.52vs0.67_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.52$rel_f_RS, DB_data_clean_0.67$rel_f_RS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.52$rel_f_cMS,DB_data_clean_0.67$rel_f_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.52$rel_f_RS ~DB_data_clean_0.52$rel_f_cMS)$coefficients[2] - lm(DB_data_clean_0.67$rel_f_RS ~DB_data_clean_0.67$rel_f_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.52$rel_f_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_0.67$rel_f_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.52$rel_f_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_0.67$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.52vs0.67_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52vs1.33
Treat_diff_F_0.52vs1.33_B <- B_0.52_Female_relMS_bootvar$t - B_1.33_Female_relMS_bootvar$t
t_Treat_diff_F_0.52vs1.33_B=mean(Treat_diff_F_0.52vs1.33_B,na.rm=TRUE)
t_Treat_diff_F_0.52vs1.33_B_lower=quantile(Treat_diff_F_0.52vs1.33_B,.025,na.rm=TRUE)
t_Treat_diff_F_0.52vs1.33_B_upper=quantile(Treat_diff_F_0.52vs1.33_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.52$rel_f_RS, DB_data_clean_1.33$rel_f_RS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.52$rel_f_cMS,DB_data_clean_1.33$rel_f_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.52$rel_f_RS ~DB_data_clean_0.52$rel_f_cMS)$coefficients[2] - lm(DB_data_clean_1.33$rel_f_RS ~DB_data_clean_1.33$rel_f_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.52$rel_f_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_1.33$rel_f_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.52$rel_f_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_1.33$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.52vs1.33_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.67vs1.33
Treat_diff_F_0.67vs1.33_B <- B_0.67_Female_relMS_bootvar$t - B_1.33_Female_relMS_bootvar$t
t_Treat_diff_F_0.67vs1.33_B=mean(Treat_diff_F_0.67vs1.33_B,na.rm=TRUE)
t_Treat_diff_F_0.67vs1.33_B_lower=quantile(Treat_diff_F_0.67vs1.33_B,.025,na.rm=TRUE)
t_Treat_diff_F_0.67vs1.33_B_upper=quantile(Treat_diff_F_0.67vs1.33_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.67$rel_f_RS, DB_data_clean_1.33$rel_f_RS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.67$rel_f_cMS,DB_data_clean_1.33$rel_f_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.67$rel_f_RS ~DB_data_clean_0.67$rel_f_cMS)$coefficients[2] - lm(DB_data_clean_1.33$rel_f_RS ~DB_data_clean_1.33$rel_f_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.67$rel_f_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_1.33$rel_f_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.67$rel_f_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_1.33$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.67vs1.33_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#S ####
#Females
#0.26vs0.52
Treat_diff_F_0.26vs0.52_S <- S_0.26_Female_relMS_bootvar$t - S_0.52_Female_relMS_bootvar$t
t_Treat_diff_F_0.26vs0.52_S=mean(Treat_diff_F_0.26vs0.52_S,na.rm=TRUE)
t_Treat_diff_F_0.26vs0.52_S_lower=quantile(Treat_diff_F_0.26vs0.52_S,.025,na.rm=TRUE)
t_Treat_diff_F_0.26vs0.52_S_upper=quantile(Treat_diff_F_0.26vs0.52_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.26$rel_f_cMS, DB_data_clean_0.52$rel_f_cMS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.26$rel_f_RS, DB_data_clean_0.52$rel_f_RS,recursive = T , use.names = F)
comb_data3=c(DB_data_clean_0.26$rel_f_cMS, DB_data_clean_0.52$rel_f_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.26$rel_f_RS ~DB_data_clean_0.26$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.26$rel_f_cMS, na.rm=TRUE)) - lm(DB_data_clean_0.52$rel_f_RS ~DB_data_clean_0.52$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.52$rel_f_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.26$rel_f_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_0.52$rel_f_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.26$rel_f_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_0.52$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.26vs0.52_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.26vs0.67
Treat_diff_F_0.26vs0.67_S <- S_0.26_Female_relMS_bootvar$t - S_0.67_Female_relMS_bootvar$t
t_Treat_diff_F_0.26vs0.67_S=mean(Treat_diff_F_0.26vs0.67_S,na.rm=TRUE)
t_Treat_diff_F_0.26vs0.67_S_lower=quantile(Treat_diff_F_0.26vs0.67_S,.025,na.rm=TRUE)
t_Treat_diff_F_0.26vs0.67_S_upper=quantile(Treat_diff_F_0.26vs0.67_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.26$rel_f_cMS, DB_data_clean_0.67$rel_f_cMS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.26$rel_f_RS, DB_data_clean_0.67$rel_f_RS,recursive = T , use.names = F)
comb_data3=c(DB_data_clean_0.26$rel_f_cMS, DB_data_clean_0.67$rel_f_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.26$rel_f_RS ~DB_data_clean_0.26$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.26$rel_f_cMS, na.rm=TRUE)) - lm(DB_data_clean_0.67$rel_f_RS ~DB_data_clean_0.67$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.67$rel_f_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.26$rel_f_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_0.67$rel_f_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.26$rel_f_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_0.67$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.26vs0.67_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.26vs1.33
Treat_diff_F_0.26vs1.33_S <- S_0.26_Female_relMS_bootvar$t - S_1.33_Female_relMS_bootvar$t
t_Treat_diff_F_0.26vs1.33_S=mean(Treat_diff_F_0.26vs1.33_S,na.rm=TRUE)
t_Treat_diff_F_0.26vs1.33_S_lower=quantile(Treat_diff_F_0.26vs1.33_S,.025,na.rm=TRUE)
t_Treat_diff_F_0.26vs1.33_S_upper=quantile(Treat_diff_F_0.26vs1.33_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.26$rel_f_cMS, DB_data_clean_1.33$rel_f_cMS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.26$rel_f_RS, DB_data_clean_1.33$rel_f_RS,recursive = T , use.names = F)
comb_data3=c(DB_data_clean_0.26$rel_f_cMS, DB_data_clean_1.33$rel_f_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.26$rel_f_RS ~DB_data_clean_0.26$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.26$rel_f_cMS, na.rm=TRUE)) - lm(DB_data_clean_1.33$rel_f_RS ~DB_data_clean_1.33$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_1.33$rel_f_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.26$rel_f_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_1.33$rel_f_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.26$rel_f_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_1.33$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.26vs1.33_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52vs0.67
Treat_diff_F_0.52vs0.67_S <- S_0.52_Female_relMS_bootvar$t - S_0.67_Female_relMS_bootvar$t
t_Treat_diff_F_0.52vs0.67_S=mean(Treat_diff_F_0.52vs0.67_S,na.rm=TRUE)
t_Treat_diff_F_0.52vs0.67_S_lower=quantile(Treat_diff_F_0.52vs0.67_S,.025,na.rm=TRUE)
t_Treat_diff_F_0.52vs0.67_S_upper=quantile(Treat_diff_F_0.52vs0.67_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.52$rel_f_cMS, DB_data_clean_0.67$rel_f_cMS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.52$rel_f_RS, DB_data_clean_0.67$rel_f_RS,recursive = T , use.names = F)
comb_data3=c(DB_data_clean_0.52$rel_f_cMS, DB_data_clean_0.67$rel_f_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.52$rel_f_RS ~DB_data_clean_0.52$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.52$rel_f_cMS, na.rm=TRUE)) - lm(DB_data_clean_0.67$rel_f_RS ~DB_data_clean_0.67$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.67$rel_f_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.52$rel_f_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_0.67$rel_f_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.52$rel_f_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_0.67$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.52vs0.67_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52vs1.33
Treat_diff_F_0.52vs1.33_S <- S_0.52_Female_relMS_bootvar$t - S_1.33_Female_relMS_bootvar$t
t_Treat_diff_F_0.52vs1.33_S=mean(Treat_diff_F_0.52vs1.33_S,na.rm=TRUE)
t_Treat_diff_F_0.52vs1.33_S_lower=quantile(Treat_diff_F_0.52vs1.33_S,.025,na.rm=TRUE)
t_Treat_diff_F_0.52vs1.33_S_upper=quantile(Treat_diff_F_0.52vs1.33_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.52$rel_f_cMS, DB_data_clean_1.33$rel_f_cMS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.52$rel_f_RS, DB_data_clean_1.33$rel_f_RS,recursive = T , use.names = F)
comb_data3=c(DB_data_clean_0.52$rel_f_cMS, DB_data_clean_1.33$rel_f_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.52$rel_f_RS ~DB_data_clean_0.52$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.52$rel_f_cMS, na.rm=TRUE)) - lm(DB_data_clean_1.33$rel_f_RS ~DB_data_clean_1.33$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_1.33$rel_f_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.52$rel_f_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_1.33$rel_f_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.52$rel_f_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_1.33$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.52vs1.33_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.67vs1.33
Treat_diff_F_0.67vs1.33_S <- S_0.67_Female_relMS_bootvar$t - S_1.33_Female_relMS_bootvar$t
t_Treat_diff_F_0.67vs1.33_S=mean(Treat_diff_F_0.67vs1.33_S,na.rm=TRUE)
t_Treat_diff_F_0.67vs1.33_S_lower=quantile(Treat_diff_F_0.67vs1.33_S,.025,na.rm=TRUE)
t_Treat_diff_F_0.67vs1.33_S_upper=quantile(Treat_diff_F_0.67vs1.33_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.67$rel_f_cMS, DB_data_clean_1.33$rel_f_cMS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.67$rel_f_RS, DB_data_clean_1.33$rel_f_RS,recursive = T , use.names = F)
comb_data3=c(DB_data_clean_0.67$rel_f_cMS, DB_data_clean_1.33$rel_f_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.67$rel_f_RS ~DB_data_clean_0.67$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.67$rel_f_cMS, na.rm=TRUE)) - lm(DB_data_clean_1.33$rel_f_RS ~DB_data_clean_1.33$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_1.33$rel_f_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.67$rel_f_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_1.33$rel_f_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.67$rel_f_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_1.33$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_F_0.67vs1.33_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Save data table ####
CompTreat_Table_Male_0.26vs0.52_I <- as.data.frame(cbind("Male", "0.26vs0.52", "Opportunity for selection", t_Treat_diff_M_0.26vs0.52_I, t_Treat_diff_M_0.26vs0.52_I_lower, t_Treat_diff_M_0.26vs0.52_I_upper, t_Treat_diff_M_0.26vs0.52_I_p))
CompTreat_Table_Male_0.26vs0.67_I <- as.data.frame(cbind("Male", "0.26vs0.67", "Opportunity for selection", t_Treat_diff_M_0.26vs0.67_I, t_Treat_diff_M_0.26vs0.67_I_lower, t_Treat_diff_M_0.26vs0.67_I_upper, t_Treat_diff_M_0.26vs0.67_I_p))
CompTreat_Table_Male_0.26vs1.33_I <- as.data.frame(cbind("Male", "0.26vs1.33", "Opportunity for selection", t_Treat_diff_M_0.26vs1.33_I, t_Treat_diff_M_0.26vs1.33_I_lower, t_Treat_diff_M_0.26vs1.33_I_upper, t_Treat_diff_M_0.26vs1.33_I_p))
CompTreat_Table_Male_0.52vs0.67_I <- as.data.frame(cbind("Male", "0.52vs0.67", "Opportunity for selection", t_Treat_diff_M_0.52vs0.67_I, t_Treat_diff_M_0.52vs0.67_I_lower, t_Treat_diff_M_0.52vs0.67_I_upper, t_Treat_diff_M_0.52vs0.67_I_p))
CompTreat_Table_Male_0.52vs1.33_I <- as.data.frame(cbind("Male", "0.52vs1.33", "Opportunity for selection", t_Treat_diff_M_0.52vs1.33_I, t_Treat_diff_M_0.52vs1.33_I_lower, t_Treat_diff_M_0.52vs1.33_I_upper, t_Treat_diff_M_0.52vs1.33_I_p))
CompTreat_Table_Male_0.67vs1.33_I <- as.data.frame(cbind("Male", "0.67vs1.33", "Opportunity for selection", t_Treat_diff_M_0.67vs1.33_I, t_Treat_diff_M_0.67vs1.33_I_lower, t_Treat_diff_M_0.67vs1.33_I_upper, t_Treat_diff_M_0.67vs1.33_I_p))
CompTreat_Table_Male_0.26vs0.52_Is <- as.data.frame(cbind("Male", "0.26vs0.52", "Opportunity for sexual selection", t_Treat_diff_M_0.26vs0.52_Is, t_Treat_diff_M_0.26vs0.52_Is_lower, t_Treat_diff_M_0.26vs0.52_Is_upper, t_Treat_diff_M_0.26vs0.52_Is_p))
CompTreat_Table_Male_0.26vs0.67_Is <- as.data.frame(cbind("Male", "0.26vs0.67", "Opportunity for sexual selection", t_Treat_diff_M_0.26vs0.67_Is, t_Treat_diff_M_0.26vs0.67_Is_lower, t_Treat_diff_M_0.26vs0.67_Is_upper, t_Treat_diff_M_0.26vs0.67_Is_p))
CompTreat_Table_Male_0.26vs1.33_Is <- as.data.frame(cbind("Male", "0.26vs1.33", "Opportunity for sexual selection", t_Treat_diff_M_0.26vs1.33_Is, t_Treat_diff_M_0.26vs1.33_Is_lower, t_Treat_diff_M_0.26vs1.33_Is_upper, t_Treat_diff_M_0.26vs1.33_Is_p))
CompTreat_Table_Male_0.52vs0.67_Is <- as.data.frame(cbind("Male", "0.52vs0.67", "Opportunity for sexual selection", t_Treat_diff_M_0.52vs0.67_Is, t_Treat_diff_M_0.52vs0.67_Is_lower, t_Treat_diff_M_0.52vs0.67_Is_upper, t_Treat_diff_M_0.52vs0.67_Is_p))
CompTreat_Table_Male_0.52vs1.33_Is <- as.data.frame(cbind("Male", "0.52vs1.33", "Opportunity for sexual selection", t_Treat_diff_M_0.52vs1.33_Is, t_Treat_diff_M_0.52vs1.33_Is_lower, t_Treat_diff_M_0.52vs1.33_Is_upper, t_Treat_diff_M_0.52vs1.33_Is_p))
CompTreat_Table_Male_0.67vs1.33_Is <- as.data.frame(cbind("Male", "0.67vs1.33", "Opportunity for sexual selection", t_Treat_diff_M_0.67vs1.33_Is, t_Treat_diff_M_0.67vs1.33_Is_lower, t_Treat_diff_M_0.67vs1.33_Is_upper, t_Treat_diff_M_0.67vs1.33_Is_p))
CompTreat_Table_Male_0.26vs0.52_B <- as.data.frame(cbind("Male", "0.26vs0.52", "Bateman gradient", t_Treat_diff_M_0.26vs0.52_B, t_Treat_diff_M_0.26vs0.52_B_lower, t_Treat_diff_M_0.26vs0.52_B_upper, t_Treat_diff_M_0.26vs0.52_B_p))
CompTreat_Table_Male_0.26vs0.67_B <- as.data.frame(cbind("Male", "0.26vs0.67", "Bateman gradient", t_Treat_diff_M_0.26vs0.67_B, t_Treat_diff_M_0.26vs0.67_B_lower, t_Treat_diff_M_0.26vs0.67_B_upper, t_Treat_diff_M_0.26vs0.67_B_p))
CompTreat_Table_Male_0.26vs1.33_B <- as.data.frame(cbind("Male", "0.26vs1.33", "Bateman gradient", t_Treat_diff_M_0.26vs1.33_B, t_Treat_diff_M_0.26vs1.33_B_lower, t_Treat_diff_M_0.26vs1.33_B_upper, t_Treat_diff_M_0.26vs1.33_B_p))
CompTreat_Table_Male_0.52vs0.67_B <- as.data.frame(cbind("Male", "0.52vs0.67", "Bateman gradient", t_Treat_diff_M_0.52vs0.67_B, t_Treat_diff_M_0.52vs0.67_B_lower, t_Treat_diff_M_0.52vs0.67_B_upper, t_Treat_diff_M_0.52vs0.67_B_p))
CompTreat_Table_Male_0.52vs1.33_B <- as.data.frame(cbind("Male", "0.52vs1.33", "Bateman gradient", t_Treat_diff_M_0.52vs1.33_B, t_Treat_diff_M_0.52vs1.33_B_lower, t_Treat_diff_M_0.52vs1.33_B_upper, t_Treat_diff_M_0.52vs1.33_B_p))
CompTreat_Table_Male_0.67vs1.33_B <- as.data.frame(cbind("Male", "0.67vs1.33", "Bateman gradient", t_Treat_diff_M_0.67vs1.33_B, t_Treat_diff_M_0.67vs1.33_B_lower, t_Treat_diff_M_0.67vs1.33_B_upper, t_Treat_diff_M_0.67vs1.33_B_p))
CompTreat_Table_Male_0.26vs0.52_S <- as.data.frame(cbind("Male", "0.26vs0.52", "Maximum standardized sexual selection differential", t_Treat_diff_M_0.26vs0.52_S, t_Treat_diff_M_0.26vs0.52_S_lower, t_Treat_diff_M_0.26vs0.52_S_upper, t_Treat_diff_M_0.26vs0.52_S_p))
CompTreat_Table_Male_0.26vs0.67_S <- as.data.frame(cbind("Male", "0.26vs0.67", "Maximum standardized sexual selection differential", t_Treat_diff_M_0.26vs0.67_S, t_Treat_diff_M_0.26vs0.67_S_lower, t_Treat_diff_M_0.26vs0.67_S_upper, t_Treat_diff_M_0.26vs0.67_S_p))
CompTreat_Table_Male_0.26vs1.33_S <- as.data.frame(cbind("Male", "0.26vs1.33", "Maximum standardized sexual selection differential", t_Treat_diff_M_0.26vs1.33_S, t_Treat_diff_M_0.26vs1.33_S_lower, t_Treat_diff_M_0.26vs1.33_S_upper, t_Treat_diff_M_0.26vs1.33_S_p))
CompTreat_Table_Male_0.52vs0.67_S <- as.data.frame(cbind("Male", "0.52vs0.67", "Maximum standardized sexual selection differential", t_Treat_diff_M_0.52vs0.67_S, t_Treat_diff_M_0.52vs0.67_S_lower, t_Treat_diff_M_0.52vs0.67_S_upper, t_Treat_diff_M_0.52vs0.67_S_p))
CompTreat_Table_Male_0.52vs1.33_S <- as.data.frame(cbind("Male", "0.52vs1.33", "Maximum standardized sexual selection differential", t_Treat_diff_M_0.52vs1.33_S, t_Treat_diff_M_0.52vs1.33_S_lower, t_Treat_diff_M_0.52vs1.33_S_upper, t_Treat_diff_M_0.52vs1.33_S_p))
CompTreat_Table_Male_0.67vs1.33_S <- as.data.frame(cbind("Male", "0.67vs1.33", "Maximum standardized sexual selection differential", t_Treat_diff_M_0.67vs1.33_S, t_Treat_diff_M_0.67vs1.33_S_lower, t_Treat_diff_M_0.67vs1.33_S_upper, t_Treat_diff_M_0.67vs1.33_S_p))
CompTreat_Table_Female_0.26vs0.52_I <- as.data.frame(cbind("Female", "0.26vs0.52", "Opportunity for selection", t_Treat_diff_F_0.26vs0.52_I, t_Treat_diff_F_0.26vs0.52_I_lower, t_Treat_diff_F_0.26vs0.52_I_upper, t_Treat_diff_F_0.26vs0.52_I_p))
CompTreat_Table_Female_0.26vs0.67_I <- as.data.frame(cbind("Female", "0.26vs0.67", "Opportunity for selection", t_Treat_diff_F_0.26vs0.67_I, t_Treat_diff_F_0.26vs0.67_I_lower, t_Treat_diff_F_0.26vs0.67_I_upper, t_Treat_diff_F_0.26vs0.67_I_p))
CompTreat_Table_Female_0.26vs1.33_I <- as.data.frame(cbind("Female", "0.26vs1.33", "Opportunity for selection", t_Treat_diff_F_0.26vs1.33_I, t_Treat_diff_F_0.26vs1.33_I_lower, t_Treat_diff_F_0.26vs1.33_I_upper, t_Treat_diff_F_0.26vs1.33_I_p))
CompTreat_Table_Female_0.52vs0.67_I <- as.data.frame(cbind("Female", "0.52vs0.67", "Opportunity for selection", t_Treat_diff_F_0.52vs0.67_I, t_Treat_diff_F_0.52vs0.67_I_lower, t_Treat_diff_F_0.52vs0.67_I_upper, t_Treat_diff_F_0.52vs0.67_I_p))
CompTreat_Table_Female_0.52vs1.33_I <- as.data.frame(cbind("Female", "0.52vs1.33", "Opportunity for selection", t_Treat_diff_F_0.52vs1.33_I, t_Treat_diff_F_0.52vs1.33_I_lower, t_Treat_diff_F_0.52vs1.33_I_upper, t_Treat_diff_F_0.52vs1.33_I_p))
CompTreat_Table_Female_0.67vs1.33_I <- as.data.frame(cbind("Female", "0.67vs1.33", "Opportunity for selection", t_Treat_diff_F_0.67vs1.33_I, t_Treat_diff_F_0.67vs1.33_I_lower, t_Treat_diff_F_0.67vs1.33_I_upper, t_Treat_diff_F_0.67vs1.33_I_p))
CompTreat_Table_Female_0.26vs0.52_Is <- as.data.frame(cbind("Female", "0.26vs0.52", "Opportunity for sexual selection", t_Treat_diff_F_0.26vs0.52_Is, t_Treat_diff_F_0.26vs0.52_Is_lower, t_Treat_diff_F_0.26vs0.52_Is_upper, t_Treat_diff_F_0.26vs0.52_Is_p))
CompTreat_Table_Female_0.26vs0.67_Is <- as.data.frame(cbind("Female", "0.26vs0.67", "Opportunity for sexual selection", t_Treat_diff_F_0.26vs0.67_Is, t_Treat_diff_F_0.26vs0.67_Is_lower, t_Treat_diff_F_0.26vs0.67_Is_upper, t_Treat_diff_F_0.26vs0.67_Is_p))
CompTreat_Table_Female_0.26vs1.33_Is <- as.data.frame(cbind("Female", "0.26vs1.33", "Opportunity for sexual selection", t_Treat_diff_F_0.26vs1.33_Is, t_Treat_diff_F_0.26vs1.33_Is_lower, t_Treat_diff_F_0.26vs1.33_Is_upper, t_Treat_diff_F_0.26vs1.33_Is_p))
CompTreat_Table_Female_0.52vs0.67_Is <- as.data.frame(cbind("Female", "0.52vs0.67", "Opportunity for sexual selection", t_Treat_diff_F_0.52vs0.67_Is, t_Treat_diff_F_0.52vs0.67_Is_lower, t_Treat_diff_F_0.52vs0.67_Is_upper, t_Treat_diff_F_0.52vs0.67_Is_p))
CompTreat_Table_Female_0.52vs1.33_Is <- as.data.frame(cbind("Female", "0.52vs1.33", "Opportunity for sexual selection", t_Treat_diff_F_0.52vs1.33_Is, t_Treat_diff_F_0.52vs1.33_Is_lower, t_Treat_diff_F_0.52vs1.33_Is_upper, t_Treat_diff_F_0.52vs1.33_Is_p))
CompTreat_Table_Female_0.67vs1.33_Is <- as.data.frame(cbind("Female", "0.67vs1.33", "Opportunity for sexual selection", t_Treat_diff_F_0.67vs1.33_Is, t_Treat_diff_F_0.67vs1.33_Is_lower, t_Treat_diff_F_0.67vs1.33_Is_upper, t_Treat_diff_F_0.67vs1.33_Is_p))
CompTreat_Table_Female_0.26vs0.52_B <- as.data.frame(cbind("Female", "0.26vs0.52", "Bateman gradient", t_Treat_diff_F_0.26vs0.52_B, t_Treat_diff_F_0.26vs0.52_B_lower, t_Treat_diff_F_0.26vs0.52_B_upper, t_Treat_diff_F_0.26vs0.52_B_p))
CompTreat_Table_Female_0.26vs0.67_B <- as.data.frame(cbind("Female", "0.26vs0.67", "Bateman gradient", t_Treat_diff_F_0.26vs0.67_B, t_Treat_diff_F_0.26vs0.67_B_lower, t_Treat_diff_F_0.26vs0.67_B_upper, t_Treat_diff_F_0.26vs0.67_B_p))
CompTreat_Table_Female_0.26vs1.33_B <- as.data.frame(cbind("Female", "0.26vs1.33", "Bateman gradient", t_Treat_diff_F_0.26vs1.33_B, t_Treat_diff_F_0.26vs1.33_B_lower, t_Treat_diff_F_0.26vs1.33_B_upper, t_Treat_diff_F_0.26vs1.33_B_p))
CompTreat_Table_Female_0.52vs0.67_B <- as.data.frame(cbind("Female", "0.52vs0.67", "Bateman gradient", t_Treat_diff_F_0.52vs0.67_B, t_Treat_diff_F_0.52vs0.67_B_lower, t_Treat_diff_F_0.52vs0.67_B_upper, t_Treat_diff_F_0.52vs0.67_B_p))
CompTreat_Table_Female_0.52vs1.33_B <- as.data.frame(cbind("Female", "0.52vs1.33", "Bateman gradient", t_Treat_diff_F_0.52vs1.33_B, t_Treat_diff_F_0.52vs1.33_B_lower, t_Treat_diff_F_0.52vs1.33_B_upper, t_Treat_diff_F_0.52vs1.33_B_p))
CompTreat_Table_Female_0.67vs1.33_B <- as.data.frame(cbind("Female", "0.67vs1.33", "Bateman gradient", t_Treat_diff_F_0.67vs1.33_B, t_Treat_diff_F_0.67vs1.33_B_lower, t_Treat_diff_F_0.67vs1.33_B_upper, t_Treat_diff_F_0.67vs1.33_B_p))
CompTreat_Table_Female_0.26vs0.52_S <- as.data.frame(cbind("Female", "0.26vs0.52", "Maximum standardized sexual selection differential", t_Treat_diff_F_0.26vs0.52_S, t_Treat_diff_F_0.26vs0.52_S_lower, t_Treat_diff_F_0.26vs0.52_S_upper, t_Treat_diff_F_0.26vs0.52_S_p))
CompTreat_Table_Female_0.26vs0.67_S <- as.data.frame(cbind("Female", "0.26vs0.67", "Maximum standardized sexual selection differential", t_Treat_diff_F_0.26vs0.67_S, t_Treat_diff_F_0.26vs0.67_S_lower, t_Treat_diff_F_0.26vs0.67_S_upper, t_Treat_diff_F_0.26vs0.67_S_p))
CompTreat_Table_Female_0.26vs1.33_S <- as.data.frame(cbind("Female", "0.26vs1.33", "Maximum standardized sexual selection differential", t_Treat_diff_F_0.26vs1.33_S, t_Treat_diff_F_0.26vs1.33_S_lower, t_Treat_diff_F_0.26vs1.33_S_upper, t_Treat_diff_F_0.26vs1.33_S_p))
CompTreat_Table_Female_0.52vs0.67_S <- as.data.frame(cbind("Female", "0.52vs0.67", "Maximum standardized sexual selection differential", t_Treat_diff_F_0.52vs0.67_S, t_Treat_diff_F_0.52vs0.67_S_lower, t_Treat_diff_F_0.52vs0.67_S_upper, t_Treat_diff_F_0.52vs0.67_S_p))
CompTreat_Table_Female_0.52vs1.33_S <- as.data.frame(cbind("Female", "0.52vs1.33", "Maximum standardized sexual selection differential", t_Treat_diff_F_0.52vs1.33_S, t_Treat_diff_F_0.52vs1.33_S_lower, t_Treat_diff_F_0.52vs1.33_S_upper, t_Treat_diff_F_0.52vs1.33_S_p))
CompTreat_Table_Female_0.67vs1.33_S <- as.data.frame(cbind("Female", "0.67vs1.33", "Maximum standardized sexual selection differential", t_Treat_diff_F_0.67vs1.33_S, t_Treat_diff_F_0.67vs1.33_S_lower, t_Treat_diff_F_0.67vs1.33_S_upper, t_Treat_diff_F_0.67vs1.33_S_p))
colnames(CompTreat_Table_Male_0.26vs0.52_I)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.26vs0.67_I)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.26vs1.33_I)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.52vs0.67_I)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.52vs1.33_I)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.67vs1.33_I)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.26vs0.52_Is)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.26vs0.67_Is)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.26vs1.33_Is)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.52vs0.67_Is)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.52vs1.33_Is)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.67vs1.33_Is)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.26vs0.52_B)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.26vs0.67_B)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.26vs1.33_B)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.52vs0.67_B)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.52vs1.33_B)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.67vs1.33_B)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.26vs0.52_S)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.26vs0.67_S)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.26vs1.33_S)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.52vs0.67_S)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.52vs1.33_S)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Male_0.67vs1.33_S)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.26vs0.52_I)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.26vs0.67_I)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.26vs1.33_I)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.52vs0.67_I)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.52vs1.33_I)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.67vs1.33_I)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.26vs0.52_Is)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.26vs0.67_Is)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.26vs1.33_Is)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.52vs0.67_Is)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.52vs1.33_Is)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.67vs1.33_Is)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.26vs0.52_B)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.26vs0.67_B)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.26vs1.33_B)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.52vs0.67_B)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.52vs1.33_B)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.67vs1.33_B)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.26vs0.52_S)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.26vs0.67_S)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.26vs1.33_S)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.52vs0.67_S)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.52vs1.33_S)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_Female_0.67vs1.33_S)<-c("Sex","Comparison","Variable","Variance","l95.CI","u95.CI","P-Value")
Table_BatemanMetrics_TreatComp <- as.data.frame(as.matrix(rbind(CompTreat_Table_Male_0.26vs0.52_I,CompTreat_Table_Male_0.26vs0.67_I,CompTreat_Table_Male_0.26vs1.33_I,CompTreat_Table_Male_0.52vs0.67_I,CompTreat_Table_Male_0.52vs1.33_I,CompTreat_Table_Male_0.67vs1.33_I,
CompTreat_Table_Male_0.26vs0.52_Is,CompTreat_Table_Male_0.26vs0.67_Is,CompTreat_Table_Male_0.26vs1.33_Is,CompTreat_Table_Male_0.52vs0.67_Is,CompTreat_Table_Male_0.52vs1.33_Is,CompTreat_Table_Male_0.67vs1.33_Is,
CompTreat_Table_Male_0.26vs0.52_B,CompTreat_Table_Male_0.26vs0.67_B,CompTreat_Table_Male_0.26vs1.33_B,CompTreat_Table_Male_0.52vs0.67_B,CompTreat_Table_Male_0.52vs1.33_B,CompTreat_Table_Male_0.67vs1.33_B,
CompTreat_Table_Male_0.26vs0.52_S,CompTreat_Table_Male_0.26vs0.67_S,CompTreat_Table_Male_0.26vs1.33_S,CompTreat_Table_Male_0.52vs0.67_S,CompTreat_Table_Male_0.52vs1.33_S,CompTreat_Table_Male_0.67vs1.33_S,
CompTreat_Table_Female_0.26vs0.52_I,CompTreat_Table_Female_0.26vs0.67_I,CompTreat_Table_Female_0.26vs1.33_I,CompTreat_Table_Female_0.52vs0.67_I,CompTreat_Table_Female_0.52vs1.33_I,CompTreat_Table_Female_0.67vs1.33_I,
CompTreat_Table_Female_0.26vs0.52_Is,CompTreat_Table_Female_0.26vs0.67_Is,CompTreat_Table_Female_0.26vs1.33_Is,CompTreat_Table_Female_0.52vs0.67_Is,CompTreat_Table_Female_0.52vs1.33_Is,CompTreat_Table_Female_0.67vs1.33_Is,
CompTreat_Table_Female_0.26vs0.52_B,CompTreat_Table_Female_0.26vs0.67_B,CompTreat_Table_Female_0.26vs1.33_B,CompTreat_Table_Female_0.52vs0.67_B,CompTreat_Table_Female_0.52vs1.33_B,CompTreat_Table_Female_0.67vs1.33_B,
CompTreat_Table_Female_0.26vs0.52_S,CompTreat_Table_Female_0.26vs0.67_S,CompTreat_Table_Female_0.26vs1.33_S,CompTreat_Table_Female_0.52vs0.67_S,CompTreat_Table_Female_0.52vs1.33_S,CompTreat_Table_Female_0.67vs1.33_S
)))
Table_BatemanMetrics_TreatComp[,4]=as.numeric(Table_BatemanMetrics_TreatComp[,4])
Table_BatemanMetrics_TreatComp[,5]=as.numeric(Table_BatemanMetrics_TreatComp[,5])
Table_BatemanMetrics_TreatComp[,6]=as.numeric(Table_BatemanMetrics_TreatComp[,6])
Table_BatemanMetrics_TreatComp[,7]=as.numeric(Table_BatemanMetrics_TreatComp[,7])
Table_BatemanMetrics_TreatComp_round=cbind(Table_BatemanMetrics_TreatComp[,c(1,2,3)],round(Table_BatemanMetrics_TreatComp[,c(4,5,6,7)],digit=3))
rownames(Table_BatemanMetrics_TreatComp_round) <- NULL#Bootstrap comparison
# Sex difference ####
#I ####
#0.26
Treat_diff_0.26_MvsF_I <- I_0.26_Male_relRS_bootvar$t - I_0.26_Female_relRS_bootvar$t
t_Treat_diff_0.26_MvsF_I=mean(Treat_diff_0.26_MvsF_I,na.rm=TRUE)
t_Treat_diff_0.26_MvsF_I_lower=quantile(Treat_diff_0.26_MvsF_I,.025,na.rm=TRUE)
t_Treat_diff_0.26_MvsF_I_upper=quantile(Treat_diff_0.26_MvsF_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.26$rel_m_RS, DB_data_clean_0.26$rel_f_RS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.26$rel_m_RS)) - var(na.omit(DB_data_clean_0.26$rel_f_RS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.26$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_0.26$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_0.26_MvsF_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52
Treat_diff_0.52_MvsF_I <- I_0.52_Male_relRS_bootvar$t - I_0.52_Female_relRS_bootvar$t
t_Treat_diff_0.52_MvsF_I=mean(Treat_diff_0.52_MvsF_I,na.rm=TRUE)
t_Treat_diff_0.52_MvsF_I_lower=quantile(Treat_diff_0.52_MvsF_I,.025,na.rm=TRUE)
t_Treat_diff_0.52_MvsF_I_upper=quantile(Treat_diff_0.52_MvsF_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.52$rel_m_RS, DB_data_clean_0.52$rel_f_RS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.52$rel_m_RS)) - var(na.omit(DB_data_clean_0.52$rel_f_RS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.52$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_0.52$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_0.52_MvsF_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.67
Treat_diff_0.67_MvsF_I <- I_0.67_Male_relRS_bootvar$t - I_0.67_Female_relRS_bootvar$t
t_Treat_diff_0.67_MvsF_I=mean(Treat_diff_0.67_MvsF_I,na.rm=TRUE)
t_Treat_diff_0.67_MvsF_I_lower=quantile(Treat_diff_0.67_MvsF_I,.025,na.rm=TRUE)
t_Treat_diff_0.67_MvsF_I_upper=quantile(Treat_diff_0.67_MvsF_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.67$rel_m_RS, DB_data_clean_0.67$rel_f_RS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.67$rel_m_RS)) - var(na.omit(DB_data_clean_0.67$rel_f_RS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.67$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_0.67$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_0.67_MvsF_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#1.33
Treat_diff_1.33_MvsF_I <- I_1.33_Male_relRS_bootvar$t - I_1.33_Female_relRS_bootvar$t
t_Treat_diff_1.33_MvsF_I=mean(Treat_diff_1.33_MvsF_I,na.rm=TRUE)
t_Treat_diff_1.33_MvsF_I_lower=quantile(Treat_diff_1.33_MvsF_I,.025,na.rm=TRUE)
t_Treat_diff_1.33_MvsF_I_upper=quantile(Treat_diff_1.33_MvsF_I,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_1.33$rel_m_RS, DB_data_clean_1.33$rel_f_RS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_1.33$rel_m_RS)) - var(na.omit(DB_data_clean_1.33$rel_f_RS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_1.33$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_1.33$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_1.33_MvsF_I_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Is ####
#0.26
Treat_diff_0.26_MvsF_Is <- Is_0.26_Male_relMS_bootvar$t - Is_0.26_Female_relMS_bootvar$t
t_Treat_diff_0.26_MvsF_Is=mean(Treat_diff_0.26_MvsF_Is,na.rm=TRUE)
t_Treat_diff_0.26_MvsF_Is_lower=quantile(Treat_diff_0.26_MvsF_Is,.025,na.rm=TRUE)
t_Treat_diff_0.26_MvsF_Is_upper=quantile(Treat_diff_0.26_MvsF_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.26$rel_m_cMS, DB_data_clean_0.26$rel_f_cMS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.26$rel_m_cMS)) - var(na.omit(DB_data_clean_0.26$rel_f_cMS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.26$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_0.26$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_0.26_MvsF_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52
Treat_diff_0.52_MvsF_Is <- Is_0.52_Male_relMS_bootvar$t - Is_0.52_Female_relMS_bootvar$t
t_Treat_diff_0.52_MvsF_Is=mean(Treat_diff_0.52_MvsF_Is,na.rm=TRUE)
t_Treat_diff_0.52_MvsF_Is_lower=quantile(Treat_diff_0.52_MvsF_Is,.025,na.rm=TRUE)
t_Treat_diff_0.52_MvsF_Is_upper=quantile(Treat_diff_0.52_MvsF_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.52$rel_m_cMS, DB_data_clean_0.52$rel_f_cMS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.52$rel_m_cMS)) - var(na.omit(DB_data_clean_0.52$rel_f_cMS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.52$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_0.52$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_0.52_MvsF_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.67
Treat_diff_0.67_MvsF_Is <- Is_0.67_Male_relMS_bootvar$t - Is_0.67_Female_relMS_bootvar$t
t_Treat_diff_0.67_MvsF_Is=mean(Treat_diff_0.67_MvsF_Is,na.rm=TRUE)
t_Treat_diff_0.67_MvsF_Is_lower=quantile(Treat_diff_0.67_MvsF_Is,.025,na.rm=TRUE)
t_Treat_diff_0.67_MvsF_Is_upper=quantile(Treat_diff_0.67_MvsF_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_0.67$rel_m_cMS, DB_data_clean_0.67$rel_f_cMS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_0.67$rel_m_cMS)) - var(na.omit(DB_data_clean_0.67$rel_f_cMS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_0.67$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_0.67$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_0.67_MvsF_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#1.33
Treat_diff_1.33_MvsF_Is <- Is_1.33_Male_relMS_bootvar$t - Is_1.33_Female_relMS_bootvar$t
t_Treat_diff_1.33_MvsF_Is=mean(Treat_diff_1.33_MvsF_Is,na.rm=TRUE)
t_Treat_diff_1.33_MvsF_Is_lower=quantile(Treat_diff_1.33_MvsF_Is,.025,na.rm=TRUE)
t_Treat_diff_1.33_MvsF_Is_upper=quantile(Treat_diff_1.33_MvsF_Is,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data=c(DB_data_clean_1.33$rel_m_cMS, DB_data_clean_1.33$rel_f_cMS,recursive = T , use.names = F)
diff.observed = var(na.omit(DB_data_clean_1.33$rel_m_cMS)) - var(na.omit(DB_data_clean_1.33$rel_f_cMS))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data), length(DB_data_clean_1.33$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data), length(DB_data_clean_1.33$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = var(na.omit(b.random)) - var(na.omit(a.random))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_1.33_MvsF_Is_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#B ####
#0.26
Treat_diff_0.26_MvsF_B <- B_0.26_Male_relMS_bootvar$t - B_0.26_Female_relMS_bootvar$t
t_Treat_diff_0.26_MvsF_B=mean(Treat_diff_0.26_MvsF_B,na.rm=TRUE)
t_Treat_diff_0.26_MvsF_B_lower=quantile(Treat_diff_0.26_MvsF_B,.025,na.rm=TRUE)
t_Treat_diff_0.26_MvsF_B_upper=quantile(Treat_diff_0.26_MvsF_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.26$rel_m_RS, DB_data_clean_0.26$rel_f_RS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.26$rel_m_cMS,DB_data_clean_0.26$rel_f_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.26$rel_m_RS ~DB_data_clean_0.26$rel_m_cMS)$coefficients[2] - lm(DB_data_clean_0.26$rel_f_RS ~DB_data_clean_0.26$rel_f_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.26$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_0.26$rel_f_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.26$rel_m_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_0.26$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_0.26_MvsF_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52
Treat_diff_0.52_MvsF_B <- B_0.52_Male_relMS_bootvar$t - B_0.52_Female_relMS_bootvar$t
t_Treat_diff_0.52_MvsF_B=mean(Treat_diff_0.52_MvsF_B,na.rm=TRUE)
t_Treat_diff_0.52_MvsF_B_lower=quantile(Treat_diff_0.52_MvsF_B,.025,na.rm=TRUE)
t_Treat_diff_0.52_MvsF_B_upper=quantile(Treat_diff_0.52_MvsF_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.52$rel_m_RS, DB_data_clean_0.52$rel_f_RS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.52$rel_m_cMS,DB_data_clean_0.52$rel_f_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.52$rel_m_RS ~DB_data_clean_0.52$rel_m_cMS)$coefficients[2] - lm(DB_data_clean_0.52$rel_f_RS ~DB_data_clean_0.52$rel_f_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.52$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_0.52$rel_f_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.52$rel_m_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_0.52$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_0.52_MvsF_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.67
Treat_diff_0.67_MvsF_B <- B_0.67_Male_relMS_bootvar$t - B_0.67_Female_relMS_bootvar$t
t_Treat_diff_0.67_MvsF_B=mean(Treat_diff_0.67_MvsF_B,na.rm=TRUE)
t_Treat_diff_0.67_MvsF_B_lower=quantile(Treat_diff_0.67_MvsF_B,.025,na.rm=TRUE)
t_Treat_diff_0.67_MvsF_B_upper=quantile(Treat_diff_0.67_MvsF_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.67$rel_m_RS, DB_data_clean_0.67$rel_f_RS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.67$rel_m_cMS,DB_data_clean_0.67$rel_f_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.67$rel_m_RS ~DB_data_clean_0.67$rel_m_cMS)$coefficients[2] - lm(DB_data_clean_0.67$rel_f_RS ~DB_data_clean_0.67$rel_f_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.67$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_0.67$rel_f_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.67$rel_m_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_0.67$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_0.67_MvsF_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#1.33
Treat_diff_1.33_MvsF_B <- B_1.33_Male_relMS_bootvar$t - B_1.33_Female_relMS_bootvar$t
t_Treat_diff_1.33_MvsF_B=mean(Treat_diff_1.33_MvsF_B,na.rm=TRUE)
t_Treat_diff_1.33_MvsF_B_lower=quantile(Treat_diff_1.33_MvsF_B,.025,na.rm=TRUE)
t_Treat_diff_1.33_MvsF_B_upper=quantile(Treat_diff_1.33_MvsF_B,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_1.33$rel_m_RS, DB_data_clean_1.33$rel_f_RS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_1.33$rel_m_cMS,DB_data_clean_1.33$rel_f_cMS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_1.33$rel_m_RS ~DB_data_clean_1.33$rel_m_cMS)$coefficients[2] - lm(DB_data_clean_1.33$rel_f_RS ~DB_data_clean_1.33$rel_f_cMS)$coefficients[2]
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_1.33$rel_m_RS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_1.33$rel_f_RS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_1.33$rel_m_cMS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_1.33$rel_f_cMS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(a.random ~c.random)$coefficients[2] - lm(b.random ~d.random)$coefficients[2]
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_1.33_MvsF_B_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#S ####
#0.26
Treat_diff_0.26_MvsF_S <- S_0.26_Male_relMS_bootvar$t - S_0.26_Female_relMS_bootvar$t
t_Treat_diff_0.26_MvsF_S=mean(Treat_diff_0.26_MvsF_S,na.rm=TRUE)
t_Treat_diff_0.26_MvsF_S_lower=quantile(Treat_diff_0.26_MvsF_S,.025,na.rm=TRUE)
t_Treat_diff_0.26_MvsF_S_upper=quantile(Treat_diff_0.26_MvsF_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.26$rel_m_cMS, DB_data_clean_0.26$rel_f_cMS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.26$rel_m_RS, DB_data_clean_0.26$rel_f_RS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.26$rel_m_RS ~DB_data_clean_0.26$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.26$rel_m_cMS, na.rm=TRUE)) - lm(DB_data_clean_0.26$rel_f_RS ~DB_data_clean_0.26$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.26$rel_f_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.26$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_0.26$rel_f_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.26$rel_m_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_0.26$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_0.26_MvsF_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.52
Treat_diff_0.52_MvsF_S <- S_0.52_Male_relMS_bootvar$t - S_0.52_Female_relMS_bootvar$t
t_Treat_diff_0.52_MvsF_S=mean(Treat_diff_0.52_MvsF_S,na.rm=TRUE)
t_Treat_diff_0.52_MvsF_S_lower=quantile(Treat_diff_0.52_MvsF_S,.025,na.rm=TRUE)
t_Treat_diff_0.52_MvsF_S_upper=quantile(Treat_diff_0.52_MvsF_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.52$rel_m_cMS, DB_data_clean_0.52$rel_f_cMS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.52$rel_m_RS, DB_data_clean_0.52$rel_f_RS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.52$rel_m_RS ~DB_data_clean_0.52$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.52$rel_m_cMS, na.rm=TRUE)) - lm(DB_data_clean_0.52$rel_f_RS ~DB_data_clean_0.52$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.52$rel_f_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.52$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_0.52$rel_f_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.52$rel_m_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_0.52$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_0.52_MvsF_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#0.67
Treat_diff_0.67_MvsF_S <- S_0.67_Male_relMS_bootvar$t - S_0.67_Female_relMS_bootvar$t
t_Treat_diff_0.67_MvsF_S=mean(Treat_diff_0.67_MvsF_S,na.rm=TRUE)
t_Treat_diff_0.67_MvsF_S_lower=quantile(Treat_diff_0.67_MvsF_S,.025,na.rm=TRUE)
t_Treat_diff_0.67_MvsF_S_upper=quantile(Treat_diff_0.67_MvsF_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_0.67$rel_m_cMS, DB_data_clean_0.67$rel_f_cMS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_0.67$rel_m_RS, DB_data_clean_0.67$rel_f_RS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_0.67$rel_m_RS ~DB_data_clean_0.67$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.67$rel_m_cMS, na.rm=TRUE)) - lm(DB_data_clean_0.67$rel_f_RS ~DB_data_clean_0.67$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_0.67$rel_f_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_0.67$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_0.67$rel_f_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_0.67$rel_m_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_0.67$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_0.67_MvsF_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#1.33
Treat_diff_1.33_MvsF_S <- S_1.33_Male_relMS_bootvar$t - S_1.33_Female_relMS_bootvar$t
t_Treat_diff_1.33_MvsF_S=mean(Treat_diff_1.33_MvsF_S,na.rm=TRUE)
t_Treat_diff_1.33_MvsF_S_lower=quantile(Treat_diff_1.33_MvsF_S,.025,na.rm=TRUE)
t_Treat_diff_1.33_MvsF_S_upper=quantile(Treat_diff_1.33_MvsF_S,.975,na.rm=TRUE)
#Permutation test to calculate p value
comb_data1=c(DB_data_clean_1.33$rel_m_cMS, DB_data_clean_1.33$rel_f_cMS,recursive = T , use.names = F)
comb_data2=c(DB_data_clean_1.33$rel_m_RS, DB_data_clean_1.33$rel_f_RS,recursive = T , use.names = F)
diff.observed = lm(DB_data_clean_1.33$rel_m_RS ~DB_data_clean_1.33$rel_m_cMS)$coefficients[2]*sqrt(var(DB_data_clean_1.33$rel_m_cMS, na.rm=TRUE)) - lm(DB_data_clean_1.33$rel_f_RS ~DB_data_clean_1.33$rel_f_cMS)$coefficients[2]*sqrt(var(DB_data_clean_1.33$rel_f_cMS, na.rm=TRUE))
diff.observed
number_of_permutations = 100000
diff.random = NULL
for (i in 1 : number_of_permutations) {
# Sample from the combined dataset
a.random = sample (na.omit(comb_data1), length(DB_data_clean_1.33$rel_m_cMS), TRUE)
b.random = sample (na.omit(comb_data1), length(DB_data_clean_1.33$rel_f_cMS), TRUE)
c.random = sample (na.omit(comb_data2), length(DB_data_clean_1.33$rel_m_RS), TRUE)
d.random = sample (na.omit(comb_data2), length(DB_data_clean_1.33$rel_f_RS), TRUE)
# Null (permuated) difference
diff.random[i] = lm(c.random ~a.random)$coefficients[2]*sqrt(var(a.random, na.rm=TRUE)) - lm(d.random ~b.random)$coefficients[2]*sqrt(var(b.random, na.rm=TRUE))
}
# P-value is the fraction of how many times the permuted difference is
# equal or more extreme than the observed difference
t_Treat_diff_1.33_MvsF_S_p = sum(abs(diff.random) >= as.numeric(abs(diff.observed)))/ number_of_permutations
#Save data table ####
CompTreat_Table_0.26_MvsF_I <- as.data.frame(cbind("Male", "0.26", "Opportunity for selection", t_Treat_diff_0.26_MvsF_I, t_Treat_diff_0.26_MvsF_I_lower, t_Treat_diff_0.26_MvsF_I_upper, t_Treat_diff_0.26_MvsF_I_p))
CompTreat_Table_0.52_MvsF_I <- as.data.frame(cbind("Male", "0.52", "Opportunity for selection", t_Treat_diff_0.52_MvsF_I, t_Treat_diff_0.52_MvsF_I_lower, t_Treat_diff_0.52_MvsF_I_upper, t_Treat_diff_0.52_MvsF_I_p))
CompTreat_Table_0.67_MvsF_I <- as.data.frame(cbind("Male", "0.67", "Opportunity for selection", t_Treat_diff_0.67_MvsF_I, t_Treat_diff_0.67_MvsF_I_lower, t_Treat_diff_0.67_MvsF_I_upper, t_Treat_diff_0.67_MvsF_I_p))
CompTreat_Table_1.33_MvsF_I <- as.data.frame(cbind("Male", "1.33", "Opportunity for selection", t_Treat_diff_1.33_MvsF_I, t_Treat_diff_1.33_MvsF_I_lower, t_Treat_diff_1.33_MvsF_I_upper, t_Treat_diff_1.33_MvsF_I_p))
CompTreat_Table_0.26_MvsF_Is <- as.data.frame(cbind("Male", "0.26", "Opportunity for sexual selection", t_Treat_diff_0.26_MvsF_Is, t_Treat_diff_0.26_MvsF_Is_lower, t_Treat_diff_0.26_MvsF_Is_upper, t_Treat_diff_0.26_MvsF_Is_p))
CompTreat_Table_0.52_MvsF_Is <- as.data.frame(cbind("Male", "0.52", "Opportunity for sexual selection", t_Treat_diff_0.52_MvsF_Is, t_Treat_diff_0.52_MvsF_Is_lower, t_Treat_diff_0.52_MvsF_Is_upper, t_Treat_diff_0.52_MvsF_Is_p))
CompTreat_Table_0.67_MvsF_Is <- as.data.frame(cbind("Male", "0.67", "Opportunity for sexual selection", t_Treat_diff_0.67_MvsF_Is, t_Treat_diff_0.67_MvsF_Is_lower, t_Treat_diff_0.67_MvsF_Is_upper, t_Treat_diff_0.67_MvsF_Is_p))
CompTreat_Table_1.33_MvsF_Is <- as.data.frame(cbind("Male", "1.33", "Opportunity for sexual selection", t_Treat_diff_1.33_MvsF_Is, t_Treat_diff_1.33_MvsF_Is_lower, t_Treat_diff_1.33_MvsF_Is_upper, t_Treat_diff_1.33_MvsF_Is_p))
CompTreat_Table_0.26_MvsF_B <- as.data.frame(cbind("Male", "0.26", "Bateman gradient", t_Treat_diff_0.26_MvsF_B, t_Treat_diff_0.26_MvsF_B_lower, t_Treat_diff_0.26_MvsF_B_upper, t_Treat_diff_0.26_MvsF_B_p))
CompTreat_Table_0.52_MvsF_B <- as.data.frame(cbind("Male", "0.52", "Bateman gradient", t_Treat_diff_0.52_MvsF_B, t_Treat_diff_0.52_MvsF_B_lower, t_Treat_diff_0.52_MvsF_B_upper, t_Treat_diff_0.52_MvsF_B_p))
CompTreat_Table_0.67_MvsF_B <- as.data.frame(cbind("Male", "0.67", "Bateman gradient", t_Treat_diff_0.67_MvsF_B, t_Treat_diff_0.67_MvsF_B_lower, t_Treat_diff_0.67_MvsF_B_upper, t_Treat_diff_0.67_MvsF_B_p))
CompTreat_Table_1.33_MvsF_B <- as.data.frame(cbind("Male", "1.33", "Bateman gradient", t_Treat_diff_1.33_MvsF_B, t_Treat_diff_1.33_MvsF_B_lower, t_Treat_diff_1.33_MvsF_B_upper, t_Treat_diff_1.33_MvsF_B_p))
CompTreat_Table_0.26_MvsF_S <- as.data.frame(cbind("Male", "0.26", "Maximum standardized sexual selection differential", t_Treat_diff_0.26_MvsF_S, t_Treat_diff_0.26_MvsF_S_lower, t_Treat_diff_0.26_MvsF_S_upper, t_Treat_diff_0.26_MvsF_S_p))
CompTreat_Table_0.52_MvsF_S <- as.data.frame(cbind("Male", "0.52", "Maximum standardized sexual selection differential", t_Treat_diff_0.52_MvsF_S, t_Treat_diff_0.52_MvsF_S_lower, t_Treat_diff_0.52_MvsF_S_upper, t_Treat_diff_0.52_MvsF_S_p))
CompTreat_Table_0.67_MvsF_S <- as.data.frame(cbind("Male", "0.67", "Maximum standardized sexual selection differential", t_Treat_diff_0.67_MvsF_S, t_Treat_diff_0.67_MvsF_S_lower, t_Treat_diff_0.67_MvsF_S_upper, t_Treat_diff_0.67_MvsF_S_p))
CompTreat_Table_1.33_MvsF_S <- as.data.frame(cbind("Male", "1.33", "Maximum standardized sexual selection differential", t_Treat_diff_1.33_MvsF_S, t_Treat_diff_1.33_MvsF_S_lower, t_Treat_diff_1.33_MvsF_S_upper, t_Treat_diff_1.33_MvsF_S_p))
colnames(CompTreat_Table_0.26_MvsF_I)<-c("Sex","Treatment","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_0.52_MvsF_I)<-c("Sex","Treatment","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_0.67_MvsF_I)<-c("Sex","Treatment","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_1.33_MvsF_I)<-c("Sex","Treatment","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_0.26_MvsF_Is)<-c("Sex","Treatment","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_0.52_MvsF_Is)<-c("Sex","Treatment","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_0.67_MvsF_Is)<-c("Sex","Treatment","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_1.33_MvsF_Is)<-c("Sex","Treatment","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_0.26_MvsF_B)<-c("Sex","Treatment","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_0.52_MvsF_B)<-c("Sex","Treatment","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_0.26_MvsF_I)<-c("Sex","Treatment","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_0.67_MvsF_B)<-c("Sex","Treatment","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_1.33_MvsF_B)<-c("Sex","Treatment","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_0.26_MvsF_S)<-c("Sex","Treatment","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_0.52_MvsF_S)<-c("Sex","Treatment","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_0.67_MvsF_S)<-c("Sex","Treatment","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_0.67_MvsF_S)<-c("Sex","Treatment","Variable","Variance","l95.CI","u95.CI","P-Value")
colnames(CompTreat_Table_1.33_MvsF_S)<-c("Sex","Treatment","Variable","Variance","l95.CI","u95.CI","P-Value")
Table_BatemanMetrics_SexComp <- as.data.frame(as.matrix(rbind(CompTreat_Table_0.26_MvsF_I,CompTreat_Table_0.52_MvsF_I,CompTreat_Table_0.67_MvsF_I,CompTreat_Table_1.33_MvsF_I,
CompTreat_Table_0.26_MvsF_Is,CompTreat_Table_0.52_MvsF_Is,CompTreat_Table_0.67_MvsF_Is,CompTreat_Table_1.33_MvsF_Is,
CompTreat_Table_0.26_MvsF_B,CompTreat_Table_0.52_MvsF_B,CompTreat_Table_0.67_MvsF_B,CompTreat_Table_1.33_MvsF_B,
CompTreat_Table_0.26_MvsF_S,CompTreat_Table_0.52_MvsF_S,CompTreat_Table_0.67_MvsF_S,CompTreat_Table_1.33_MvsF_S)))
Table_BatemanMetrics_SexComp[,4]=as.numeric(Table_BatemanMetrics_SexComp[,4])
Table_BatemanMetrics_SexComp[,5]=as.numeric(Table_BatemanMetrics_SexComp[,5])
Table_BatemanMetrics_SexComp[,6]=as.numeric(Table_BatemanMetrics_SexComp[,6])
Table_BatemanMetrics_SexComp[,7]=as.numeric(Table_BatemanMetrics_SexComp[,7])
Table_BatemanMetrics_SexComp_round=cbind(Table_BatemanMetrics_SexComp[,c(1,2,3)],round(Table_BatemanMetrics_SexComp[,c(4,5,6,7)],digit=3))
rownames(Table_BatemanMetrics_SexComp_round) <- NULLOpportunity for selection
BarPlot_1<- ggplot(Table_BatemanMetrics[c(1:4,17:20),], aes(x=Sex, y=Variance, fill=Treatment)) +
scale_y_continuous(limits = c(0, 2), breaks = seq(0,2,0.5), expand = c(0 ,0))+
geom_hline(yintercept=0, linetype="solid", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8)+
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
xlab('') +ylab(expression(paste(~italic("I"))))+ggtitle('Opportunity for selection')+labs(tag = "A")+
scale_x_discrete(breaks=waiver(),labels = c("Female","Male"))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
plot.margin = margin(0.1,2,0.1,0.2,"cm"),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.tag.position=c(0.01,0.98),
legend.position = c(1.05, 0.8),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
scale_fill_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))
BarPlot_1
Figure 7: Effects of denstiy treatment on the opportunity for selection
(variance in reproductive success) in females and males. Means and 95%
confidence intervals.
Treatement comparisons via permutation
test for the opportunity for selection
Table_BatemanMetrics_TreatComp_round[c(1,2,3,4,5,6,25,26,27,28,29,30),c(1,3,4,5,6)] Sex Variable Variance l95.CI u95.CI
1 Male Opportunity for selection 0.131 -0.334 0.707
2 Male Opportunity for selection -0.196 -1.090 0.599
3 Male Opportunity for selection -0.169 -0.765 0.480
4 Male Opportunity for selection -0.327 -1.137 0.250
5 Male Opportunity for selection -0.300 -0.759 0.128
6 Male Opportunity for selection 0.027 -0.684 0.896
25 Female Opportunity for selection 0.062 -0.198 0.362
26 Female Opportunity for selection -0.142 -0.476 0.193
27 Female Opportunity for selection 0.298 0.065 0.558
28 Female Opportunity for selection -0.204 -0.583 0.153
29 Female Opportunity for selection 0.236 -0.068 0.528
30 Female Opportunity for selection 0.439 0.099 0.787Sex comparisons via permutation test for the opportunity for selection
Table_BatemanMetrics_SexComp_round[c(1,2,3,4),c(1,3,4,5,6)] Sex Variable Variance l95.CI u95.CI
1 Male Opportunity for selection -0.032 -0.480 0.527
2 Male Opportunity for selection -0.101 -0.382 0.226
3 Male Opportunity for selection 0.022 -0.600 0.847
4 Male Opportunity for selection 0.435 0.026 0.902Opportunity for sexual selection
BarPlot_2<- ggplot(Table_BatemanMetrics[c(5:8,21:24),], aes(x=Sex, y=Variance, fill=Treatment)) +
scale_y_continuous(limits = c(0, 1.2), breaks = seq(0,1.2,0.2), expand = c(0 ,0))+
geom_hline(yintercept=0, linetype="solid", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8)+
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
xlab('') +ylab(expression(paste(~italic("I"['s']))))+ggtitle('Opportunity for sexual selection')+labs(tag = "B")+
scale_x_discrete(breaks=waiver(),labels = c("Female","Male"))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
plot.margin = margin(0.1,2,0.1,0.2,"cm"),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.tag.position=c(0.01,0.98),
legend.position = c(1.05, 0.8),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
scale_fill_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))
BarPlot_2
Figure 8: Effects of denstiy and area treatment on the opportunity for
sexual selection (variance in mating success) in females and males.
Means and 95% confidence intervals.
Treatement comparisons via
permutation test for the opportunity for sexual selection
Table_BatemanMetrics_TreatComp_round[c(7,8,9,10,11,12,31,32,33,34,35,36),c(1,3,4,5,6)] Sex Variable Variance l95.CI u95.CI
7 Male Opportunity for sexual selection -0.116 -0.320 0.062
8 Male Opportunity for sexual selection 0.006 -0.091 0.101
9 Male Opportunity for sexual selection -0.024 -0.174 0.109
10 Male Opportunity for sexual selection 0.122 -0.053 0.324
11 Male Opportunity for sexual selection 0.092 -0.119 0.312
12 Male Opportunity for sexual selection -0.030 -0.176 0.095
31 Female Opportunity for sexual selection -0.164 -0.406 0.042
32 Female Opportunity for sexual selection -0.003 -0.101 0.075
33 Female Opportunity for sexual selection -0.042 -0.198 0.085
34 Female Opportunity for sexual selection 0.162 -0.054 0.411
35 Female Opportunity for sexual selection 0.123 -0.130 0.397
36 Female Opportunity for sexual selection -0.039 -0.205 0.105Sex comparisons via permutation test for the opportunity for selection
Table_BatemanMetrics_SexComp_round[c(5,6,7,8),c(1,3,4,5,6)] Sex Variable Variance l95.CI u95.CI
5 Male Opportunity for sexual selection 0.003 -0.081 0.090
6 Male Opportunity for sexual selection -0.045 -0.340 0.237
7 Male Opportunity for sexual selection -0.006 -0.110 0.087
8 Male Opportunity for sexual selection -0.014 -0.199 0.165Bateman gradient
# Bateman gradient
BarPlot_3<- ggplot(Table_BatemanMetrics[c(9:12,25:28),], aes(x=Sex, y=Variance, fill=Treatment)) +
geom_hline(yintercept=0, linetype="solid", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8)+
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
xlab('Sex') +ylab(expression(paste(~italic(symbol("b")['ss']))))+ggtitle('Bateman gradient')+labs(tag = "C")+
scale_x_discrete(breaks=waiver(),labels = c("Female","Male"))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
plot.margin = margin(0.1,2,0.1,0.2,"cm"),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.tag.position=c(0.01,0.98),
legend.position = c(1.05, 0.8),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
scale_fill_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))
BarPlot_3
Figure 9: Effects of density treatment on the Bateman gradient (slope of
mating success on reproductive success) in females and males. Means and
95% confidence intervals.
Treatement comparisons via permutation
test for the Bateman gradient
Table_BatemanMetrics_TreatComp_round[c(13,14,15,16,17,18,37,38,39,40,41,42),c(1,3,4,5,6)] Sex Variable Variance l95.CI u95.CI
13 Male Bateman gradient 0.902 -0.105 1.958
14 Male Bateman gradient -0.571 -1.823 0.849
15 Male Bateman gradient -0.252 -1.338 0.684
16 Male Bateman gradient -1.474 -2.747 -0.073
17 Male Bateman gradient -1.155 -2.241 -0.246
18 Male Bateman gradient 0.319 -1.109 1.491
37 Female Bateman gradient -0.094 -1.094 0.795
38 Female Bateman gradient 1.228 -0.197 2.551
39 Female Bateman gradient 0.066 -1.017 1.070
40 Female Bateman gradient 1.322 0.008 2.530
41 Female Bateman gradient 0.160 -0.760 1.005
42 Female Bateman gradient -1.162 -2.442 0.191Sex comparisons via permutation test for the opportunity for selection
Table_BatemanMetrics_SexComp_round[c(9,10,11,12),c(1,3,4,5,6)] Sex Variable Variance l95.CI u95.CI
9 Male Bateman gradient 0.037 -0.999 1.148
10 Male Bateman gradient -0.959 -1.907 -0.105
11 Male Bateman gradient 1.836 0.196 3.315
12 Male Bateman gradient 0.355 -0.590 1.402# Bateman gradient (scatter)
p1<-ggplot(DB_data_clean_0.26, aes(x=rel_m_cMS, y=rel_m_RS)) +
geom_point(alpha=0.4,shape=16, size = 3,color=colpal2[2]) +
geom_smooth(method=lm, se=TRUE,alpha=0.3) +
theme(plot.tag.position=c(0.1,0.98))+
labs(tag = "A")+xlab('Rel. mating success')+ylab("Rel. reproductive success")+ggtitle('Small gr. size & large area')+ theme(plot.title = element_text(hjust = 0.5))+
theme(axis.text=element_text(size=13),
axis.title=element_text(size=14))+ theme(legend.position="none")+
ylim(0,4.2)+xlim(0,3.2)+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"))
p1=p1+geom_point(aes(x=rel_f_cMS, y=rel_f_RS),color=colpal2[1],alpha=0.4,shape=16, size = 3)+
geom_smooth(aes(x=rel_f_cMS, y=rel_f_RS),color=colpal2[1],method=lm, se=TRUE,alpha=0.3)
p2<-ggplot(DB_data_clean_0.52, aes(x=rel_m_cMS, y=rel_m_RS)) +
geom_point(alpha=0.4,shape=16, size = 3,color=colpal2[2]) +
geom_smooth(method=lm, se=TRUE,alpha=0.3) +
theme(plot.tag.position=c(0.1,0.98))+
labs(tag = "B")+xlab('Rel. mating success')+ylab("Rel. reproductive success")+ggtitle('Large gr. size & large area')+ theme(plot.title = element_text(hjust = 0.5))+
theme(axis.text=element_text(size=13),
axis.title=element_text(size=14))+ theme(legend.position="none")+
ylim(0,4.2)+xlim(0,3.2)+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"))
p2=p2+geom_point(aes(x=rel_f_cMS, y=rel_f_RS),color=colpal2[1],alpha=0.4,shape=16, size = 3)+
geom_smooth(aes(x=rel_f_cMS, y=rel_f_RS),color=colpal2[1],method=lm, se=TRUE,alpha=0.3)
p3<-ggplot(DB_data_clean_0.67, aes(x=rel_m_cMS, y=rel_m_RS)) +
geom_point(alpha=0.4,shape=16, size = 3,color=colpal2[2]) +
geom_smooth(method=lm, se=TRUE,alpha=0.3) +
theme(plot.tag.position=c(0.1,0.98))+
labs(tag = "C")+xlab('Rel. mating success')+ylab("Rel. reproductive success")+ggtitle('Small gr. size & small area')+ theme(plot.title = element_text(hjust = 0.5))+
theme(axis.text=element_text(size=13),
axis.title=element_text(size=14))+ theme(legend.position="none")+
ylim(0,4.2)+xlim(0,3.2)+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"))
p3=p3+geom_point(aes(x=rel_f_cMS, y=rel_f_RS),color=colpal2[1],alpha=0.4,shape=16, size = 3)+
geom_smooth(aes(x=rel_f_cMS, y=rel_f_RS),color=colpal2[1],method=lm, se=TRUE,alpha=0.3)
p4<-ggplot(DB_data_clean_1.33, aes(x=rel_m_cMS, y=rel_m_RS)) +
geom_point(alpha=0.4,shape=16, size = 3,color=colpal2[2]) +
geom_smooth(method=lm, se=TRUE,alpha=0.3) +
theme(plot.tag.position=c(0.1,0.98))+
labs(tag = "D")+xlab('Rel. mating success')+ylab("Rel. reproductive success")+ggtitle('Large gr. size & small area')+ theme(plot.title = element_text(hjust = 0.5))+
theme(axis.text=element_text(size=13),
axis.title=element_text(size=14))+
ylim(0,4.2)+xlim(0,3.2)+
theme(legend.position="none")+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"))
p4=p4+geom_point(aes(x=rel_f_cMS, y=rel_f_RS),color=colpal2[1],alpha=0.4,shape=16, size = 3)+
geom_smooth(aes(x=rel_f_cMS, y=rel_f_RS),color=colpal2[1],method=lm, se=TRUE,alpha=0.3)
#Create legend
p5<-ggplot(DB_data_clean, aes(x=Total_N_MTP1, y=Total_N_Rd, color=Sex)) +
geom_point(alpha=0.4,shape=16, size = 3, position=position_jitterdodge(jitter.height=0,jitter.width=0,dodge.width = 0)) +
geom_smooth(method=lm, se=TRUE,alpha=0.3) +
scale_color_manual(values=c(colpal2[1],colpal2[2]),name = "Sex", labels = c('Females','Males'))+
xlab('Rel. mating success')+ylab("Rel. reproductive success")+
guides(color=guide_legend(override.aes=list(fill=NA)))+
theme(legend.key = element_rect(fill = "transparent"))
legend <- get_legend(p5)
plot1<-grid.arrange(p1,p2,legend,p3,p4,legend, nrow = 2,ncol=3, widths=c(2.3, 2.3, 0.65))
Figure 10: Scatter plot of the Bateman gradient in females and males.
Means and 95% confidence intervals.
Jones index
BarPlot_4<- ggplot(Table_BatemanMetrics[c(13:16,25:28),], aes(x=Sex, y=Variance, fill=Treatment)) +
geom_hline(yintercept=0, linetype="solid", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8)+
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
xlab('Sex') +ylab(expression(paste(~italic("s'"['max']))))+ggtitle('Jones index')+labs(tag = "D")+
scale_x_discrete(breaks=waiver(),labels = c("Female","Male"))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
plot.margin = margin(0.1,2,0.1,0.2,"cm"),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.tag.position=c(0.01,0.98),
legend.position = c(1.05, 0.8),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
scale_fill_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))
BarPlot_4
Figure 11: Effects of density treatment on the Jones index (maximum
strength of sexual selection) in females and males. Means and 95%
confidence intervals.
Treatement comparisons via permutation
test for the Jones index
Table_BatemanMetrics_TreatComp_round[c(19,20,21,22,23,25,43,44,45,46,47,48),c(1,3,4,5,6)] Sex Variable Variance l95.CI
19 Male Maximum standardized sexual selection differential 0.323 -0.137
20 Male Maximum standardized sexual selection differential -0.209 -0.756
21 Male Maximum standardized sexual selection differential -0.113 -0.443
22 Male Maximum standardized sexual selection differential -0.532 -1.127
23 Male Maximum standardized sexual selection differential -0.436 -0.836
25 Female Opportunity for selection 0.062 -0.198
43 Female Maximum standardized sexual selection differential -0.178 -0.554
44 Female Maximum standardized sexual selection differential 0.451 -0.054
45 Female Maximum standardized sexual selection differential -0.002 -0.378
46 Female Maximum standardized sexual selection differential 0.628 0.143
47 Female Maximum standardized sexual selection differential 0.176 -0.177
48 Female Maximum standardized sexual selection differential -0.453 -0.880
u95.CI
19 0.767
20 0.337
21 0.222
22 0.066
23 -0.015
25 0.362
43 0.209
44 0.906
45 0.370
46 1.067
47 0.535
48 0.025Sex comparisons via permutation test for the opportunity for selection
Table_BatemanMetrics_SexComp_round[c(13,14,15,16),c(1,3,4,5,6)] Sex Variable Variance l95.CI
13 Male Maximum standardized sexual selection differential 0.014 -0.368
14 Male Maximum standardized sexual selection differential -0.487 -0.909
15 Male Maximum standardized sexual selection differential 0.674 0.052
16 Male Maximum standardized sexual selection differential 0.125 -0.191
u95.CI
13 0.414
14 -0.028
15 1.276
16 0.454Variance decopmposition
We decomposed the variance in reproductive success for males and
females.
Components fro males were:
- Mating success
-
Insemination success
- Fertilization success
- Partner
fecundity
Components for females were:
- Mating success
- Fecundity
We used bootstrapping (10.000 bootstrap
replicates) to obtain 95% confidence intervals and permutation tests
(10.000 permutations) to statistically compare treatments and
sexes.
# Bootstrapping variances + CI ####
# mMS ####
# small group - large area
DB_data_clean_0.26_M_MS_n <-as.data.table(DB_data_clean_0.26$rel_m_cMS)
c <- function(d, i){
d2 <- d[i,]
return(var(d2[,1], na.rm=TRUE))
}
D0.26_M_MS_bootvar <- boot(DB_data_clean_0.26_M_MS_n, c, R=10000)
# Large group + large Area
DB_data_clean_0.52_M_MS_n <-as.data.table(DB_data_clean_0.52$rel_m_cMS)
D0.52_M_MS_bootvar <- boot(DB_data_clean_0.52_M_MS_n, c, R=10000)
# Small group + small Area
DB_data_clean_0.67_M_MS_n <-as.data.table(DB_data_clean_0.67$rel_m_cMS)
D0.67_M_MS_bootvar <- boot(DB_data_clean_0.67_M_MS_n, c, R=10000)
# Large group + small Area
DB_data_clean_1.33_M_MS_n <-as.data.table(DB_data_clean_1.33$rel_m_cMS)
D1.33_M_MS_bootvar <- boot(DB_data_clean_1.33_M_MS_n, c, R=10000)
rm("c")
# InSuc ####
# small group - large area
DB_data_clean_0.26_M_InSuc_n <-as.data.table(DB_data_clean_0.26$rel_m_InSuc)
c <- function(d, i){
d2 <- d[i,]
return(var(d2[,1], na.rm=TRUE))
}
D0.26_M_InSuc_bootvar <- boot(DB_data_clean_0.26_M_InSuc_n, c, R=10000)
# Large group + large Area
DB_data_clean_0.52_M_InSuc_n <-as.data.table(DB_data_clean_0.52$rel_m_InSuc)
D0.52_M_InSuc_bootvar <- boot(DB_data_clean_0.52_M_InSuc_n, c, R=10000)
# Small group + small Area
DB_data_clean_0.67_M_InSuc_n <-as.data.table(DB_data_clean_0.67$rel_m_InSuc)
D0.67_M_InSuc_bootvar <- boot(DB_data_clean_0.67_M_InSuc_n, c, R=10000)
# Large group + small Area
DB_data_clean_1.33_M_InSuc_n <-as.data.table(DB_data_clean_1.33$rel_m_InSuc)
D1.33_M_InSuc_bootvar <- boot(DB_data_clean_1.33_M_InSuc_n, c, R=10000)
rm("c")
# feSuc ####
# small group - large area
DB_data_clean_0.26_M_feSuc_n <-as.data.table(DB_data_clean_0.26$rel_m_feSuc)
c <- function(d, i){
d2 <- d[i,]
return(var(d2$V1, na.rm=TRUE))
}
D0.26_M_feSuc_bootvar <- boot(DB_data_clean_0.26_M_feSuc_n, c, R=10000)
# Large group + large Area
DB_data_clean_0.52_M_feSuc_n <-as.data.table(DB_data_clean_0.52$rel_m_feSuc)
D0.52_M_feSuc_bootvar <- boot(DB_data_clean_0.52_M_feSuc_n, c, R=10000)
# Small group + small Area
DB_data_clean_0.67_M_feSuc_n <-as.data.table(DB_data_clean_0.67$rel_m_feSuc)
D0.67_M_feSuc_bootvar <- boot(DB_data_clean_0.67_M_feSuc_n, c, R=10000)
# Large group + small Area
DB_data_clean_1.33_M_feSuc_n <-as.data.table(DB_data_clean_1.33$rel_m_feSuc)
D1.33_M_feSuc_bootvar <- boot(DB_data_clean_1.33_M_feSuc_n, c, R=10000)
rm("c")
# pFec ####
# small group - large area
DB_data_clean_0.26_M_pFec_n <-as.data.table(DB_data_clean_0.26$rel_m_pFec)
c <- function(d, i){
d2 <- d[i,]
return(var(d2[,1], na.rm=TRUE))
}
D0.26_M_pFec_bootvar <- boot(DB_data_clean_0.26_M_pFec_n, c, R=10000)
# Large group + large Area
DB_data_clean_0.52_M_pFec_n <-as.data.table(DB_data_clean_0.52$rel_m_pFec)
D0.52_M_pFec_bootvar <- boot(DB_data_clean_0.52_M_pFec_n, c, R=10000)
# Small group + small Area
DB_data_clean_0.67_M_pFec_n <-as.data.table(DB_data_clean_0.67$rel_m_pFec)
D0.67_M_pFec_bootvar <- boot(DB_data_clean_0.67_M_pFec_n, c, R=10000)
# Large group + small Area
DB_data_clean_1.33_M_pFec_n <-as.data.table(DB_data_clean_1.33$rel_m_pFec)
D1.33_M_pFec_bootvar <- boot(DB_data_clean_1.33_M_pFec_n, c, R=10000)
rm("c")
# fMS ####
# small group - large area
DB_data_clean_0.26_F_fMS_n <-as.data.table(DB_data_clean_0.26$rel_f_cMS)
c <- function(d, i){
d2 <- d[i,]
return(var(d2[,1], na.rm=TRUE))
}
D0.26_F_fMS_bootvar <- boot(DB_data_clean_0.26_F_fMS_n, c, R=10000)
# Large group + large Area
DB_data_clean_0.52_F_fMS_n <-as.data.table(DB_data_clean_0.52$rel_f_cMS)
D0.52_F_fMS_bootvar <- boot(DB_data_clean_0.52_F_fMS_n, c, R=10000)
# Small group + small Area
DB_data_clean_0.67_F_fMS_n <-as.data.table(DB_data_clean_0.67$rel_f_cMS)
D0.67_F_fMS_bootvar <- boot(DB_data_clean_0.67_F_fMS_n, c, R=10000)
# Large group + small Area
DB_data_clean_1.33_F_fMS_n <-as.data.table(DB_data_clean_1.33$rel_f_cMS)
D1.33_F_fMS_bootvar <- boot(DB_data_clean_1.33_F_fMS_n, c, R=10000)
rm("c")
# fFec ####
# small group - large area
DB_data_clean_0.26_F_fFec_n <-as.data.table(DB_data_clean_0.26$rel_f_fec_pMate)
c <- function(d, i){
d2 <- d[i,]
return(var(d2[,1], na.rm=TRUE))
}
D0.26_F_fFec_bootvar <- boot(DB_data_clean_0.26_F_fFec_n, c, R=10000)
# Large group + large Area
DB_data_clean_0.52_F_fFec_n <-as.data.table(DB_data_clean_0.52$rel_f_fec_pMate)
D0.52_F_fFec_bootvar <- boot(DB_data_clean_0.52_F_fFec_n, c, R=10000)
# Small group + small Area
DB_data_clean_0.67_F_fFec_n <-as.data.table(DB_data_clean_0.67$rel_f_fec_pMate)
D0.67_F_fFec_bootvar <- boot(DB_data_clean_0.67_F_fFec_n, c, R=10000)
# Large group + small Area
DB_data_clean_1.33_F_fFec_n <-as.data.table(DB_data_clean_1.33$rel_f_fec_pMate)
D1.33_F_fFec_bootvar <- boot(DB_data_clean_1.33_F_fFec_n, c, R=10000)
rm("c")
#Write Table ####
library(base)
PhenVarBoot_Table_Male_0.26_MS <- as.data.frame(cbind("Male", "MS", "0.26", mean(D0.26_M_MS_bootvar$t), quantile(D0.26_M_MS_bootvar$t,.025, names = FALSE), quantile(D0.26_M_MS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.52_MS <- as.data.frame(cbind("Male", "MS", "0.52", mean(D0.52_M_MS_bootvar$t), quantile(D0.52_M_MS_bootvar$t,.025, names = FALSE), quantile(D0.52_M_MS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.67_MS <- as.data.frame(cbind("Male", "MS", "0.67", mean(D0.67_M_MS_bootvar$t), quantile(D0.67_M_MS_bootvar$t,.025, names = FALSE), quantile(D0.67_M_MS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_1.33_MS <- as.data.frame(cbind("Male", "MS", "1.33", mean(D1.33_M_MS_bootvar$t), quantile(D1.33_M_MS_bootvar$t,.025, names = FALSE), quantile(D1.33_M_MS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.26_InSuc <- as.data.frame(cbind("Male", "InSuc", "0.26", mean(D0.26_M_InSuc_bootvar$t), quantile(D0.26_M_InSuc_bootvar$t,.025, names = FALSE), quantile(D0.26_M_InSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.52_InSuc <- as.data.frame(cbind("Male", "InSuc", "0.52", mean(D0.52_M_InSuc_bootvar$t), quantile(D0.52_M_InSuc_bootvar$t,.025, names = FALSE), quantile(D0.52_M_InSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.67_InSuc <- as.data.frame(cbind("Male", "InSuc", "0.67", mean(D0.67_M_InSuc_bootvar$t), quantile(D0.67_M_InSuc_bootvar$t,.025, names = FALSE), quantile(D0.67_M_InSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_1.33_InSuc <- as.data.frame(cbind("Male", "InSuc", "1.33", mean(D1.33_M_InSuc_bootvar$t), quantile(D1.33_M_InSuc_bootvar$t,.025, names = FALSE), quantile(D1.33_M_InSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.26_feSuc <- as.data.frame(cbind("Male", "feSuc", "0.26", mean(D0.26_M_feSuc_bootvar$t), quantile(D0.26_M_feSuc_bootvar$t,.025, names = FALSE), quantile(D0.26_M_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.52_feSuc <- as.data.frame(cbind("Male", "feSuc", "0.52", mean(D0.52_M_feSuc_bootvar$t), quantile(D0.52_M_feSuc_bootvar$t,.025, names = FALSE), quantile(D0.52_M_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.67_feSuc <- as.data.frame(cbind("Male", "feSuc", "0.67", mean(D0.67_M_feSuc_bootvar$t), quantile(D0.67_M_feSuc_bootvar$t,.025, names = FALSE), quantile(D0.67_M_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_1.33_feSuc <- as.data.frame(cbind("Male", "feSuc", "1.33", mean(D1.33_M_feSuc_bootvar$t), quantile(D1.33_M_feSuc_bootvar$t,.025, names = FALSE), quantile(D1.33_M_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.26_pFec <- as.data.frame(cbind("Male", "pFec", "0.26", mean(D0.26_M_pFec_bootvar$t), quantile(D0.26_M_pFec_bootvar$t,.025, names = FALSE), quantile(D0.26_M_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.52_pFec <- as.data.frame(cbind("Male", "pFec", "0.52", mean(D0.52_M_pFec_bootvar$t), quantile(D0.52_M_pFec_bootvar$t,.025, names = FALSE), quantile(D0.52_M_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.67_pFec <- as.data.frame(cbind("Male", "pFec", "0.67", mean(D0.67_M_pFec_bootvar$t), quantile(D0.67_M_pFec_bootvar$t,.025, names = FALSE), quantile(D0.67_M_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_1.33_pFec <- as.data.frame(cbind("Male", "pFec", "1.33", mean(D1.33_M_pFec_bootvar$t), quantile(D1.33_M_pFec_bootvar$t,.025, names = FALSE), quantile(D1.33_M_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_0.26_fMS <- as.data.frame(cbind("Female", "fMS", "0.26", mean(D0.26_F_fMS_bootvar$t), quantile(D0.26_F_fMS_bootvar$t,.025, names = FALSE), quantile(D0.26_F_fMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_0.52_fMS <- as.data.frame(cbind("Female", "fMS", "0.52", mean(D0.52_F_fMS_bootvar$t), quantile(D0.52_F_fMS_bootvar$t,.025, names = FALSE), quantile(D0.52_F_fMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_0.67_fMS <- as.data.frame(cbind("Female", "fMS", "0.67", mean(D0.67_F_fMS_bootvar$t), quantile(D0.67_F_fMS_bootvar$t,.025, names = FALSE), quantile(D0.67_F_fMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_1.33_fMS <- as.data.frame(cbind("Female", "fMS", "1.33", mean(D1.33_F_fMS_bootvar$t), quantile(D1.33_F_fMS_bootvar$t,.025, names = FALSE), quantile(D1.33_F_fMS_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_0.26_fFec <- as.data.frame(cbind("Female", "fFec", "0.26", mean(D0.26_F_fFec_bootvar$t), quantile(D0.26_F_fFec_bootvar$t,.025, names = FALSE), quantile(D0.26_F_fFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_0.52_fFec <- as.data.frame(cbind("Female", "fFec", "0.52", mean(D0.52_F_fFec_bootvar$t), quantile(D0.52_F_fFec_bootvar$t,.025, names = FALSE), quantile(D0.52_F_fFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_0.67_fFec <- as.data.frame(cbind("Female", "fFec", "0.67", mean(D0.67_F_fFec_bootvar$t), quantile(D0.67_F_fFec_bootvar$t,.025, names = FALSE), quantile(D0.67_F_fFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_1.33_fFec <- as.data.frame(cbind("Female", "fFec", "1.33", mean(D1.33_F_fFec_bootvar$t,na.rm=T), quantile(D1.33_F_fFec_bootvar$t,.025, names = FALSE,na.rm=T), quantile(D1.33_F_fFec_bootvar$t,.975, names = FALSE,na.rm=T)))
PhenVarBoot_Table <- as.data.frame(as.matrix(rbind(PhenVarBoot_Table_Male_0.26_MS,PhenVarBoot_Table_Male_0.52_MS,PhenVarBoot_Table_Male_0.67_MS,PhenVarBoot_Table_Male_1.33_MS,
PhenVarBoot_Table_Male_0.26_InSuc,PhenVarBoot_Table_Male_0.52_InSuc,PhenVarBoot_Table_Male_0.67_InSuc,PhenVarBoot_Table_Male_1.33_InSuc,
PhenVarBoot_Table_Male_0.26_feSuc,PhenVarBoot_Table_Male_0.52_feSuc,PhenVarBoot_Table_Male_0.67_feSuc,PhenVarBoot_Table_Male_1.33_feSuc,
PhenVarBoot_Table_Male_0.26_pFec,PhenVarBoot_Table_Male_0.52_pFec,PhenVarBoot_Table_Male_0.67_pFec,PhenVarBoot_Table_Male_1.33_pFec,
PhenVarBoot_Table_Female_0.26_fMS,PhenVarBoot_Table_Female_0.52_fMS,PhenVarBoot_Table_Female_0.67_fMS,PhenVarBoot_Table_Female_1.33_fMS,
PhenVarBoot_Table_Female_0.26_fFec,PhenVarBoot_Table_Female_0.52_fFec,PhenVarBoot_Table_Female_0.67_fFec,PhenVarBoot_Table_Female_1.33_fFec)))
is.table(PhenVarBoot_Table)
colnames(PhenVarBoot_Table)[1] <- "Sex"
colnames(PhenVarBoot_Table)[2] <- "Trait"
colnames(PhenVarBoot_Table)[3] <- "Density"
colnames(PhenVarBoot_Table)[4] <- "Variance"
colnames(PhenVarBoot_Table)[5] <- "l95.CI"
colnames(PhenVarBoot_Table)[6] <- "u95.CI"
PhenVarBoot_Table[,4]=as.numeric(PhenVarBoot_Table[,4])
PhenVarBoot_Table[,5]=as.numeric(PhenVarBoot_Table[,5])
PhenVarBoot_Table[,6]=as.numeric(PhenVarBoot_Table[,6])
PhenVarBoot_Table_round=cbind(PhenVarBoot_Table[,c(1,2,3)],round(PhenVarBoot_Table[,c(4,5,6)],digit=3))
rownames(PhenVarBoot_Table_round) <- NULL#Compute covariace matrices
# Small group + large Area ####
#Covariance mMS x inSuc
x5=as.data.frame(cbind(DB_data_clean_0.26_M_MS_n,DB_data_clean_0.26_M_InSuc_n))
c <- function(d, i){
d2 <- d[i,]
return(cov(d2[1],d2[2],use='pairwise.complete.obs'))
}
D0.26_M_cov_mMS_inSuc_bootvar <- boot(x5, c, R=10000)
#Covariance mMS x feSuc
x6=as.data.frame(cbind(DB_data_clean_0.26_M_MS_n,DB_data_clean_0.26_M_feSuc_n))
D0.26_M_cov_mMS_feSuc_bootvar <- boot(x6, c, R=10000)
#Covariance mMS x pFec
x7=as.data.frame(cbind(DB_data_clean_0.26_M_MS_n,DB_data_clean_0.26_M_pFec_n))
D0.26_M_cov_mMS_pFec_bootvar <- boot(x7, c, R=10000)
#Covariance inSuc x feSuc
x8=as.data.frame(cbind(DB_data_clean_0.26_M_InSuc_n,DB_data_clean_0.26_M_feSuc_n))
D0.26_M_cov_inSuc_feSuc_bootvar <- boot(x8, c, R=10000)
#Covariance inSuc x pFec
x9=as.data.frame(cbind(DB_data_clean_0.26_M_InSuc_n,DB_data_clean_0.26_M_pFec_n))
D0.26_M_cov_inSuc_pFec_bootvar <- boot(x9, c, R=10000)
#Covariance feSuc x pFec
x10=as.data.frame(cbind(DB_data_clean_0.26_M_feSuc_n,DB_data_clean_0.26_M_pFec_n))
D0.26_M_cov_feSuc_pFec_bootvar <- boot(x10, c, R=10000)
#Covariance fMS x fFec
x13=as.data.frame(cbind(DB_data_clean_0.26_F_fMS_n,DB_data_clean_0.26_F_fFec_n))
D0.26_F_cov_fMS_fFec_bootvar <- boot(x13, c, R=10000)
rm("c")
#Write Table ####
PhenVarBoot_Table_Male_0.26_cov_mMS_inSuc <- as.data.frame(cbind("Male", "cov_mMS_inSuc", "0.26", mean(D0.26_M_cov_mMS_inSuc_bootvar$t), quantile(D0.26_M_cov_mMS_inSuc_bootvar$t,.025, names = FALSE), quantile(D0.26_M_cov_mMS_inSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.26_cov_mMS_feSuc <- as.data.frame(cbind("Male", "cov_mMS_feSuc", "0.26", mean(D0.26_M_cov_mMS_feSuc_bootvar$t,na.rm=T), quantile(D0.26_M_cov_mMS_feSuc_bootvar$t,.025, names = FALSE,na.rm=T), quantile(D0.26_M_cov_mMS_feSuc_bootvar$t,.975, names = FALSE,na.rm=T)))
PhenVarBoot_Table_Male_0.26_cov_mMS_pFec <- as.data.frame(cbind("Male", "cov_mMS_pFec", "0.26", mean(D0.26_M_cov_mMS_pFec_bootvar$t,na.rm=T), quantile(D0.26_M_cov_mMS_pFec_bootvar$t,.025, names = FALSE,na.rm=T), quantile(D0.26_M_cov_mMS_pFec_bootvar$t,.975, names = FALSE,na.rm=T)))
PhenVarBoot_Table_Male_0.26_cov_inSuc_feSuc <- as.data.frame(cbind("Male", "cov_inSuc_feSuc", "0.26", mean(D0.26_M_cov_inSuc_feSuc_bootvar$t,na.rm=T), quantile(D0.26_M_cov_inSuc_feSuc_bootvar$t,.025, names = FALSE,na.rm=T), quantile(D0.26_M_cov_inSuc_feSuc_bootvar$t,.975, names = FALSE,na.rm=T)))
PhenVarBoot_Table_Male_0.26_cov_inSuc_pFec <- as.data.frame(cbind("Male", "cov_inSuc_pFec", "0.26", mean(D0.26_M_cov_inSuc_pFec_bootvar$t,na.rm=T), quantile(D0.26_M_cov_inSuc_pFec_bootvar$t,.025, names = FALSE,na.rm=T), quantile(D0.26_M_cov_inSuc_pFec_bootvar$t,.975, names = FALSE,na.rm=T)))
PhenVarBoot_Table_Male_0.26_cov_feSuc_pFec <- as.data.frame(cbind("Male", "cov_feSuc_pFec", "0.26", mean(D0.26_M_cov_feSuc_pFec_bootvar$t,na.rm=T), quantile(D0.26_M_cov_feSuc_pFec_bootvar$t,.025, names = FALSE,na.rm=T), quantile(D0.26_M_cov_feSuc_pFec_bootvar$t,.975, names = FALSE,na.rm=T)))
PhenVarBoot_Table_Female_0.26_cov_fMS_fFec <- as.data.frame(cbind("Female", "cov_fMS_fFec", "0.26", mean(D0.26_F_cov_fMS_fFec_bootvar$t,na.rm=T), quantile(D0.26_F_cov_fMS_fFec_bootvar$t,.025, names = FALSE,na.rm=T), quantile(D0.26_F_cov_fMS_fFec_bootvar$t,.975, names = FALSE,na.rm=T)))
PhenVarBoot_Cov_Table_0.26 <- as.data.frame(as.matrix(rbind(PhenVarBoot_Table_Male_0.26_cov_mMS_inSuc,PhenVarBoot_Table_Male_0.26_cov_mMS_feSuc,
PhenVarBoot_Table_Male_0.26_cov_mMS_pFec,PhenVarBoot_Table_Male_0.26_cov_inSuc_feSuc,
PhenVarBoot_Table_Male_0.26_cov_inSuc_pFec,PhenVarBoot_Table_Male_0.26_cov_feSuc_pFec,
PhenVarBoot_Table_Female_0.26_cov_fMS_fFec)),digits=3)
is.table(PhenVarBoot_Cov_Table_0.26)
colnames(PhenVarBoot_Cov_Table_0.26)[1] <- "Sex"
colnames(PhenVarBoot_Cov_Table_0.26)[2] <- "Trait"
colnames(PhenVarBoot_Cov_Table_0.26)[3] <- "Density"
colnames(PhenVarBoot_Cov_Table_0.26)[4] <- "Variance"
colnames(PhenVarBoot_Cov_Table_0.26)[5] <- "l95.CI"
colnames(PhenVarBoot_Cov_Table_0.26)[6] <- "u95.CI"
PhenVarBoot_Cov_Table_0.26[,4]=as.numeric(PhenVarBoot_Cov_Table_0.26[,4])
PhenVarBoot_Cov_Table_0.26[,5]=as.numeric(PhenVarBoot_Cov_Table_0.26[,5])
PhenVarBoot_Cov_Table_0.26[,6]=as.numeric(PhenVarBoot_Cov_Table_0.26[,6])
PhenVarBoot_Cov_Table_0.26_round=cbind(PhenVarBoot_Cov_Table_0.26[,1:3],round(PhenVarBoot_Cov_Table_0.26[,4:6],digit=3))
rownames(PhenVarBoot_Cov_Table_0.26_round) <- NULL
# Large group + large Area ####
#Covariance mMS x inSuc
x5=as.data.frame(cbind(DB_data_clean_0.52_M_MS_n,DB_data_clean_0.52_M_InSuc_n))
c <- function(d, i){
d2 <- d[i,]
return(cov(d2[1],d2[2],use='pairwise.complete.obs'))
}
D0.52_M_cov_mMS_inSuc_bootvar <- boot(x5, c, R=10000)
#Covariance mMS x feSuc
x6=as.data.frame(cbind(DB_data_clean_0.52_M_MS_n,DB_data_clean_0.52_M_feSuc_n))
D0.52_M_cov_mMS_feSuc_bootvar <- boot(x6, c, R=10000)
#Covariance mMS x pFec
x7=as.data.frame(cbind(DB_data_clean_0.52_M_MS_n,DB_data_clean_0.52_M_pFec_n))
D0.52_M_cov_mMS_pFec_bootvar <- boot(x7, c, R=10000)
#Covariance inSuc x feSuc
x8=as.data.frame(cbind(DB_data_clean_0.52_M_InSuc_n,DB_data_clean_0.52_M_feSuc_n))
D0.52_M_cov_inSuc_feSuc_bootvar <- boot(x8, c, R=10000)
#Covariance inSuc x pFec
x9=as.data.frame(cbind(DB_data_clean_0.52_M_InSuc_n,DB_data_clean_0.52_M_pFec_n))
D0.52_M_cov_inSuc_pFec_bootvar <- boot(x9, c, R=10000)
#Covariance feSuc x pFec
x10=as.data.frame(cbind(DB_data_clean_0.52_M_feSuc_n,DB_data_clean_0.52_M_pFec_n))
D0.52_M_cov_feSuc_pFec_bootvar <- boot(x10, c, R=10000)
#Covariance fMS x fFec
x13=as.data.frame(cbind(DB_data_clean_0.52_F_fMS_n,DB_data_clean_0.52_F_fFec_n))
D0.52_F_cov_fMS_fFec_bootvar <- boot(x13, c, R=10000)
rm("c")
#Write Table ####
PhenVarBoot_Table_Male_0.52_cov_mMS_inSuc <- as.data.frame(cbind("Male", "cov_mMS_inSuc", "0.52", mean(D0.52_M_cov_mMS_inSuc_bootvar$t), quantile(D0.52_M_cov_mMS_inSuc_bootvar$t,.025, names = FALSE), quantile(D0.52_M_cov_mMS_inSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.52_cov_mMS_feSuc <- as.data.frame(cbind("Male", "cov_mMS_feSuc", "0.52", mean(D0.52_M_cov_mMS_feSuc_bootvar$t), quantile(D0.52_M_cov_mMS_feSuc_bootvar$t,.025, names = FALSE), quantile(D0.52_M_cov_mMS_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.52_cov_mMS_pFec <- as.data.frame(cbind("Male", "cov_mMS_pFec", "0.52", mean(D0.52_M_cov_mMS_pFec_bootvar$t), quantile(D0.52_M_cov_mMS_pFec_bootvar$t,.025, names = FALSE), quantile(D0.52_M_cov_mMS_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.52_cov_inSuc_feSuc <- as.data.frame(cbind("Male", "cov_inSuc_feSuc", "0.52", mean(D0.52_M_cov_inSuc_feSuc_bootvar$t), quantile(D0.52_M_cov_inSuc_feSuc_bootvar$t,.025, names = FALSE), quantile(D0.52_M_cov_inSuc_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.52_cov_inSuc_pFec <- as.data.frame(cbind("Male", "cov_inSuc_pFec", "0.52", mean(D0.52_M_cov_inSuc_pFec_bootvar$t), quantile(D0.52_M_cov_inSuc_pFec_bootvar$t,.025, names = FALSE), quantile(D0.52_M_cov_inSuc_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.52_cov_feSuc_pFec <- as.data.frame(cbind("Male", "cov_feSuc_pFec", "0.52", mean(D0.52_M_cov_feSuc_pFec_bootvar$t), quantile(D0.52_M_cov_feSuc_pFec_bootvar$t,.025, names = FALSE), quantile(D0.52_M_cov_feSuc_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_0.52_cov_fMS_fFec <- as.data.frame(cbind("Female", "cov_fMS_fFec", "0.52", mean(D0.52_F_cov_fMS_fFec_bootvar$t), quantile(D0.52_F_cov_fMS_fFec_bootvar$t,.025, names = FALSE), quantile(D0.52_F_cov_fMS_fFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Cov_Table_0.52 <- as.data.frame(as.matrix(rbind(PhenVarBoot_Table_Male_0.52_cov_mMS_inSuc,PhenVarBoot_Table_Male_0.52_cov_mMS_feSuc,
PhenVarBoot_Table_Male_0.52_cov_mMS_pFec,PhenVarBoot_Table_Male_0.52_cov_inSuc_feSuc,
PhenVarBoot_Table_Male_0.52_cov_inSuc_pFec,PhenVarBoot_Table_Male_0.52_cov_feSuc_pFec,
PhenVarBoot_Table_Female_0.52_cov_fMS_fFec)),digits=3)
is.table(PhenVarBoot_Cov_Table_0.52)
colnames(PhenVarBoot_Cov_Table_0.52)[1] <- "Sex"
colnames(PhenVarBoot_Cov_Table_0.52)[2] <- "Trait"
colnames(PhenVarBoot_Cov_Table_0.52)[3] <- "Density"
colnames(PhenVarBoot_Cov_Table_0.52)[4] <- "Variance"
colnames(PhenVarBoot_Cov_Table_0.52)[5] <- "l95.CI"
colnames(PhenVarBoot_Cov_Table_0.52)[6] <- "u95.CI"
PhenVarBoot_Cov_Table_0.52[,4]=as.numeric(PhenVarBoot_Cov_Table_0.52[,4])
PhenVarBoot_Cov_Table_0.52[,5]=as.numeric(PhenVarBoot_Cov_Table_0.52[,5])
PhenVarBoot_Cov_Table_0.52[,6]=as.numeric(PhenVarBoot_Cov_Table_0.52[,6])
PhenVarBoot_Cov_Table_0.52_round=cbind(PhenVarBoot_Cov_Table_0.52[,1:3],round(PhenVarBoot_Cov_Table_0.52[,4:6],digit=3))
rownames(PhenVarBoot_Cov_Table_0.52_round) <- NULL
# Small group + small Area ####
#Covariance mMS x inSuc
x5=as.data.frame(cbind(DB_data_clean_0.67_M_MS_n,DB_data_clean_0.67_M_InSuc_n))
c <- function(d, i){
d2 <- d[i,]
return(cov(d2[1],d2[2],use='pairwise.complete.obs'))
}
D0.67_M_cov_mMS_inSuc_bootvar <- boot(x5, c, R=10000)
#Covariance mMS x feSuc
x6=as.data.frame(cbind(DB_data_clean_0.67_M_MS_n,DB_data_clean_0.67_M_feSuc_n))
D0.67_M_cov_mMS_feSuc_bootvar <- boot(x6, c, R=10000)
#Covariance mMS x pFec
x7=as.data.frame(cbind(DB_data_clean_0.67_M_MS_n,DB_data_clean_0.67_M_pFec_n))
D0.67_M_cov_mMS_pFec_bootvar <- boot(x7, c, R=10000)
#Covariance inSuc x feSuc
x8=as.data.frame(cbind(DB_data_clean_0.67_M_InSuc_n,DB_data_clean_0.67_M_feSuc_n))
D0.67_M_cov_inSuc_feSuc_bootvar <- boot(x8, c, R=10000)
#Covariance inSuc x pFec
x9=as.data.frame(cbind(DB_data_clean_0.67_M_InSuc_n,DB_data_clean_0.67_M_pFec_n))
D0.67_M_cov_inSuc_pFec_bootvar <- boot(x9, c, R=10000)
#Covariance feSuc x pFec
x10=as.data.frame(cbind(DB_data_clean_0.67_M_feSuc_n,DB_data_clean_0.67_M_pFec_n))
D0.67_M_cov_feSuc_pFec_bootvar <- boot(x10, c, R=10000)
#Covariance fMS x fFec
x13=as.data.frame(cbind(DB_data_clean_0.67_F_fMS_n,DB_data_clean_0.67_F_fFec_n))
D0.67_F_cov_fMS_fFec_bootvar <- boot(x13, c, R=10000)
rm("c")
#Write Table ####
PhenVarBoot_Table_Male_0.67_cov_mMS_inSuc <- as.data.frame(cbind("Male", "cov_mMS_inSuc", "0.67", mean(D0.67_M_cov_mMS_inSuc_bootvar$t), quantile(D0.67_M_cov_mMS_inSuc_bootvar$t,.025, names = FALSE), quantile(D0.67_M_cov_mMS_inSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.67_cov_mMS_feSuc <- as.data.frame(cbind("Male", "cov_mMS_feSuc", "0.67", mean(D0.67_M_cov_mMS_feSuc_bootvar$t), quantile(D0.67_M_cov_mMS_feSuc_bootvar$t,.025, names = FALSE), quantile(D0.67_M_cov_mMS_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.67_cov_mMS_pFec <- as.data.frame(cbind("Male", "cov_mMS_pFec", "0.67", mean(D0.67_M_cov_mMS_pFec_bootvar$t), quantile(D0.67_M_cov_mMS_pFec_bootvar$t,.025, names = FALSE), quantile(D0.67_M_cov_mMS_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.67_cov_inSuc_feSuc <- as.data.frame(cbind("Male", "cov_inSuc_feSuc", "0.67", mean(D0.67_M_cov_inSuc_feSuc_bootvar$t), quantile(D0.67_M_cov_inSuc_feSuc_bootvar$t,.025, names = FALSE), quantile(D0.67_M_cov_inSuc_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.67_cov_inSuc_pFec <- as.data.frame(cbind("Male", "cov_inSuc_pFec", "0.67", mean(D0.67_M_cov_inSuc_pFec_bootvar$t), quantile(D0.67_M_cov_inSuc_pFec_bootvar$t,.025, names = FALSE), quantile(D0.67_M_cov_inSuc_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_0.67_cov_feSuc_pFec <- as.data.frame(cbind("Male", "cov_feSuc_pFec", "0.67", mean(D0.67_M_cov_feSuc_pFec_bootvar$t), quantile(D0.67_M_cov_feSuc_pFec_bootvar$t,.025, names = FALSE), quantile(D0.67_M_cov_feSuc_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_0.67_cov_fMS_fFec <- as.data.frame(cbind("Female", "cov_fMS_fFec", "0.67", mean(D0.67_F_cov_fMS_fFec_bootvar$t), quantile(D0.67_F_cov_fMS_fFec_bootvar$t,.025, names = FALSE), quantile(D0.67_F_cov_fMS_fFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Cov_Table_0.67 <- as.data.frame(as.matrix(rbind(PhenVarBoot_Table_Male_0.67_cov_mMS_inSuc,PhenVarBoot_Table_Male_0.67_cov_mMS_feSuc,
PhenVarBoot_Table_Male_0.67_cov_mMS_pFec,PhenVarBoot_Table_Male_0.67_cov_inSuc_feSuc,
PhenVarBoot_Table_Male_0.67_cov_inSuc_pFec,PhenVarBoot_Table_Male_0.67_cov_feSuc_pFec,
PhenVarBoot_Table_Female_0.67_cov_fMS_fFec)),digits=3)
is.table(PhenVarBoot_Cov_Table_0.67)
colnames(PhenVarBoot_Cov_Table_0.67)[1] <- "Sex"
colnames(PhenVarBoot_Cov_Table_0.67)[2] <- "Trait"
colnames(PhenVarBoot_Cov_Table_0.67)[3] <- "Density"
colnames(PhenVarBoot_Cov_Table_0.67)[4] <- "Variance"
colnames(PhenVarBoot_Cov_Table_0.67)[5] <- "l95.CI"
colnames(PhenVarBoot_Cov_Table_0.67)[6] <- "u95.CI"
PhenVarBoot_Cov_Table_0.67[,4]=as.numeric(PhenVarBoot_Cov_Table_0.67[,4])
PhenVarBoot_Cov_Table_0.67[,5]=as.numeric(PhenVarBoot_Cov_Table_0.67[,5])
PhenVarBoot_Cov_Table_0.67[,6]=as.numeric(PhenVarBoot_Cov_Table_0.67[,6])
PhenVarBoot_Cov_Table_0.67_round=cbind(PhenVarBoot_Cov_Table_0.67[,1:3],round(PhenVarBoot_Cov_Table_0.67[,4:6],digit=3))
rownames(PhenVarBoot_Cov_Table_0.67_round) <- NULL
# Large group + small Area ####
#Covariance mMS x inSuc
x5=as.data.frame(cbind(DB_data_clean_1.33_M_MS_n,DB_data_clean_1.33_M_InSuc_n))
c <- function(d, i){
d2 <- d[i,]
return(cov(d2[1],d2[2],use='pairwise.complete.obs'))
}
D1.33_M_cov_mMS_inSuc_bootvar <- boot(x5, c, R=10000)
#Covariance mMS x feSuc
x6=as.data.frame(cbind(DB_data_clean_1.33_M_MS_n,DB_data_clean_1.33_M_feSuc_n))
D1.33_M_cov_mMS_feSuc_bootvar <- boot(x6, c, R=10000)
#Covariance mMS x pFec
x7=as.data.frame(cbind(DB_data_clean_1.33_M_MS_n,DB_data_clean_1.33_M_pFec_n))
D1.33_M_cov_mMS_pFec_bootvar <- boot(x7, c, R=10000)
#Covariance inSuc x feSuc
x8=as.data.frame(cbind(DB_data_clean_1.33_M_InSuc_n,DB_data_clean_1.33_M_feSuc_n))
D1.33_M_cov_inSuc_feSuc_bootvar <- boot(x8, c, R=10000)
#Covariance inSuc x pFec
x9=as.data.frame(cbind(DB_data_clean_1.33_M_InSuc_n,DB_data_clean_1.33_M_pFec_n))
D1.33_M_cov_inSuc_pFec_bootvar <- boot(x9, c, R=10000)
#Covariance feSuc x pFec
x10=as.data.frame(cbind(DB_data_clean_1.33_M_feSuc_n,DB_data_clean_1.33_M_pFec_n))
D1.33_M_cov_feSuc_pFec_bootvar <- boot(x10, c, R=10000)
#Covariance fMS x fFec
x13=as.data.frame(cbind(DB_data_clean_1.33_F_fMS_n,DB_data_clean_1.33_F_fFec_n))
D1.33_F_cov_fMS_fFec_bootvar <- boot(x13, c, R=10000)
rm("c")
#Write Table ####
PhenVarBoot_Table_Male_1.33_cov_mMS_inSuc <- as.data.frame(cbind("Male", "cov_mMS_inSuc", "1.33", mean(D1.33_M_cov_mMS_inSuc_bootvar$t), quantile(D1.33_M_cov_mMS_inSuc_bootvar$t,.025, names = FALSE), quantile(D1.33_M_cov_mMS_inSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_1.33_cov_mMS_feSuc <- as.data.frame(cbind("Male", "cov_mMS_feSuc", "1.33", mean(D1.33_M_cov_mMS_feSuc_bootvar$t), quantile(D1.33_M_cov_mMS_feSuc_bootvar$t,.025, names = FALSE), quantile(D1.33_M_cov_mMS_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_1.33_cov_mMS_pFec <- as.data.frame(cbind("Male", "cov_mMS_pFec", "1.33", mean(D1.33_M_cov_mMS_pFec_bootvar$t), quantile(D1.33_M_cov_mMS_pFec_bootvar$t,.025, names = FALSE), quantile(D1.33_M_cov_mMS_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_1.33_cov_inSuc_feSuc <- as.data.frame(cbind("Male", "cov_inSuc_feSuc", "1.33", mean(D1.33_M_cov_inSuc_feSuc_bootvar$t), quantile(D1.33_M_cov_inSuc_feSuc_bootvar$t,.025, names = FALSE), quantile(D1.33_M_cov_inSuc_feSuc_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_1.33_cov_inSuc_pFec <- as.data.frame(cbind("Male", "cov_inSuc_pFec", "1.33", mean(D1.33_M_cov_inSuc_pFec_bootvar$t), quantile(D1.33_M_cov_inSuc_pFec_bootvar$t,.025, names = FALSE), quantile(D1.33_M_cov_inSuc_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Male_1.33_cov_feSuc_pFec <- as.data.frame(cbind("Male", "cov_feSuc_pFec", "1.33", mean(D1.33_M_cov_feSuc_pFec_bootvar$t), quantile(D1.33_M_cov_feSuc_pFec_bootvar$t,.025, names = FALSE), quantile(D1.33_M_cov_feSuc_pFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Table_Female_1.33_cov_fMS_fFec <- as.data.frame(cbind("Female", "cov_fMS_fFec", "1.33", mean(D1.33_F_cov_fMS_fFec_bootvar$t), quantile(D1.33_F_cov_fMS_fFec_bootvar$t,.025, names = FALSE), quantile(D1.33_F_cov_fMS_fFec_bootvar$t,.975, names = FALSE)))
PhenVarBoot_Cov_Table_1.33 <- as.data.frame(as.matrix(rbind(PhenVarBoot_Table_Male_1.33_cov_mMS_inSuc,PhenVarBoot_Table_Male_1.33_cov_mMS_feSuc,
PhenVarBoot_Table_Male_1.33_cov_mMS_pFec,PhenVarBoot_Table_Male_1.33_cov_inSuc_feSuc,
PhenVarBoot_Table_Male_1.33_cov_inSuc_pFec,PhenVarBoot_Table_Male_1.33_cov_feSuc_pFec,
PhenVarBoot_Table_Female_1.33_cov_fMS_fFec)),digits=3)
is.table(PhenVarBoot_Cov_Table_1.33)
colnames(PhenVarBoot_Cov_Table_1.33)[1] <- "Sex"
colnames(PhenVarBoot_Cov_Table_1.33)[2] <- "Trait"
colnames(PhenVarBoot_Cov_Table_1.33)[3] <- "Density"
colnames(PhenVarBoot_Cov_Table_1.33)[4] <- "Variance"
colnames(PhenVarBoot_Cov_Table_1.33)[5] <- "l95.CI"
colnames(PhenVarBoot_Cov_Table_1.33)[6] <- "u95.CI"
PhenVarBoot_Cov_Table_1.33[,4]=as.numeric(PhenVarBoot_Cov_Table_1.33[,4])
PhenVarBoot_Cov_Table_1.33[,5]=as.numeric(PhenVarBoot_Cov_Table_1.33[,5])
PhenVarBoot_Cov_Table_1.33[,6]=as.numeric(PhenVarBoot_Cov_Table_1.33[,6])
PhenVarBoot_Cov_Table_1.33_round=cbind(PhenVarBoot_Cov_Table_1.33[,1:3],round(PhenVarBoot_Cov_Table_1.33[,4:6],digit=3))
rownames(PhenVarBoot_Cov_Table_1.33_round) <- NULL
PhenVarBoot_Table_plot_cov <- as.data.frame(as.matrix(rbind( PhenVarBoot_Table_Male_0.26_cov_mMS_inSuc,PhenVarBoot_Table_Male_0.52_cov_mMS_inSuc,
PhenVarBoot_Table_Male_0.67_cov_mMS_inSuc,PhenVarBoot_Table_Male_1.33_cov_mMS_inSuc,
PhenVarBoot_Table_Male_0.26_cov_mMS_feSuc,PhenVarBoot_Table_Male_0.52_cov_mMS_feSuc,
PhenVarBoot_Table_Male_0.67_cov_mMS_feSuc,PhenVarBoot_Table_Male_1.33_cov_mMS_feSuc,
PhenVarBoot_Table_Male_0.26_cov_mMS_pFec,PhenVarBoot_Table_Male_0.52_cov_mMS_pFec,
PhenVarBoot_Table_Male_0.67_cov_mMS_pFec,PhenVarBoot_Table_Male_1.33_cov_mMS_pFec,
PhenVarBoot_Table_Male_0.26_cov_inSuc_feSuc,PhenVarBoot_Table_Male_0.52_cov_inSuc_feSuc,
PhenVarBoot_Table_Male_0.67_cov_inSuc_feSuc,PhenVarBoot_Table_Male_1.33_cov_inSuc_feSuc,
PhenVarBoot_Table_Male_0.26_cov_inSuc_pFec,PhenVarBoot_Table_Male_0.52_cov_inSuc_pFec,
PhenVarBoot_Table_Male_0.67_cov_inSuc_pFec,PhenVarBoot_Table_Male_1.33_cov_inSuc_pFec,
PhenVarBoot_Table_Male_0.26_cov_feSuc_pFec,PhenVarBoot_Table_Male_0.52_cov_feSuc_pFec,
PhenVarBoot_Table_Male_0.67_cov_feSuc_pFec,PhenVarBoot_Table_Male_1.33_cov_feSuc_pFec,
PhenVarBoot_Table_Female_0.26_cov_fMS_fFec,PhenVarBoot_Table_Female_0.52_cov_fMS_fFec,
PhenVarBoot_Table_Female_0.67_cov_fMS_fFec,PhenVarBoot_Table_Female_1.33_cov_fMS_fFec)))
is.table(PhenVarBoot_Table_plot_cov)
colnames(PhenVarBoot_Table_plot_cov)[1] <- "Sex"
colnames(PhenVarBoot_Table_plot_cov)[2] <- "Trait"
colnames(PhenVarBoot_Table_plot_cov)[3] <- "Density"
colnames(PhenVarBoot_Table_plot_cov)[4] <- "Variance"
colnames(PhenVarBoot_Table_plot_cov)[5] <- "l95.CI"
colnames(PhenVarBoot_Table_plot_cov)[6] <- "u95.CI"
PhenVarBoot_Table_plot_cov[,4]=as.numeric(PhenVarBoot_Table_plot_cov[,4])
PhenVarBoot_Table_plot_cov[,5]=as.numeric(PhenVarBoot_Table_plot_cov[,5])
PhenVarBoot_Table_plot_cov[,6]=as.numeric(PhenVarBoot_Table_plot_cov[,6])
PhenVarBoot_Table_plot_cov_round=cbind(PhenVarBoot_Table_plot_cov[,1:3],round(PhenVarBoot_Table_plot_cov[,4:6],digit=3))
PhenVarBoot_Table_plot_cov$Density<- factor(PhenVarBoot_Table_plot_cov$Density, levels=c("0.26",'0.52','0.67','1.33'))
PhenVarBoot_Table_plot_cov$Trait <- factor(PhenVarBoot_Table_plot_cov$Trait, levels=c("cov_mMS_inSuc",'cov_mMS_feSuc','cov_mMS_pFec','cov_inSuc_feSuc','cov_inSuc_pFec','cov_feSuc_pFec','cov_fMS_fFec'))
rownames(PhenVarBoot_Table_plot_cov) <- NULLVariance decomposition
PhenVarBoot_Table$Density<- factor(PhenVarBoot_Table$Density, levels=c("0.26",'0.52','0.67','1.33'))
PhenVarBoot_Table$Trait <- factor(PhenVarBoot_Table$Trait, levels=c('MS','InSuc','feSuc','pFec','fMS','fFec'))
BarPlot_1<- ggplot(PhenVarBoot_Table[1:16,], aes(x=Trait, y=Variance, fill=Density)) +
scale_y_continuous(limits = c(0, 0.8), breaks = seq(0,0.8,0.2), expand = c(0 ,0)) +
geom_hline(yintercept=0, linetype="solid", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8) +
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
ylab('Variance') +xlab('') +ggtitle('Male')+labs(tag = "A")+
scale_x_discrete(breaks=waiver(),labels = c('MS','inSuc','feSuc','Fec'))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
plot.margin = margin(0.1,2,0.1,0.2,"cm"),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.tag.position=c(0.01,0.98),
legend.position = c(1.05, 0.8),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
scale_fill_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))
BarPlot_2<- ggplot(PhenVarBoot_Table[17:24,], aes(x=Trait, y=Variance, fill=Density)) +
scale_y_continuous(limits = c(0, 2.7), breaks = seq(0,2.7,0.75), expand = c(0 ,0)) +
geom_hline(yintercept=0, linetype="solid", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8) +
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
ylab('Variance') +xlab('Variance component') +ggtitle('Female')+labs(tag = "B")+
scale_x_discrete(breaks=waiver(),labels = c("MS","PS" ,"Fec"))+
theme(panel.border = element_blank(),
plot.margin = margin(0.1,2,0.1,0.2,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.tag.position=c(0.01,0.98),
legend.position = c(1.05, 0.8),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
scale_fill_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))
plot1<-grid.arrange(BarPlot_1,BarPlot_2, nrow = 2,ncol=1)
Figure 12: Variance decomposition for males (A) into mating success,
insemination success, fertilization success and fecundity of the
partners and females (B) into mating success and fecundity. Means and
95% confidence intervals.
BarPlot_1<- ggplot(PhenVarBoot_Table_plot_cov[1:24,], aes(x=Trait, y=Variance, fill=Density)) +
geom_hline(yintercept=0, linetype="solid", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8) +
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
ylab('Variance') +xlab('') +ggtitle('Male')+labs(tag = "A")+
scale_x_discrete(breaks=waiver(),labels = c('cov\n(MS, inSuc)','cov\n(MS, feSuc)','cov\n(MS, Fec)','cov\n(inSuc, feSuc)','cov\n(inSuc,Fec)','cov\n(feSuc, Fec)'))+
theme(panel.border = element_blank(),
plot.title = element_text(hjust = 0.5),
plot.margin = margin(0.1,2.3,0.1,0.2,"cm"),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.tag.position=c(0.01,0.98),
legend.position = c(1.05, 0.8),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
scale_fill_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))
BarPlot_2<- ggplot(PhenVarBoot_Table_plot_cov[25:28,], aes(x=Trait, y=Variance, fill=Density)) +
geom_hline(yintercept=0, linetype="solid", color = "black", size=1) +
geom_bar(stat="identity", color="black", position=position_dodge(), alpha=0.8) +
geom_errorbar(aes(ymin=l95.CI, ymax=u95.CI), width=.3,size=1, position=position_dodge(.9)) +
ylab('Variance') +xlab('Variance component') +ggtitle('Female')+labs(tag = "B")+
scale_x_discrete(breaks=waiver(),labels = c('cov\n(MS, Fec)'))+
theme(panel.border = element_blank(),
plot.margin = margin(0.1,2.3,0.1,0.2,"cm"),
plot.title = element_text(hjust = 0.5),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.tag.position=c(0.01,0.98),
legend.position = c(1.05, 0.8),
legend.text = element_text(colour="black", size=10),
axis.line.x = element_line(colour = "black", size = 1),
axis.line.y = element_line(colour = "black", size = 1),
axis.text.x = element_text(face="plain", color="black", size=16, angle=0),
axis.text.y = element_text(face="plain", color="black", size=16, angle=0),
axis.title.x = element_text(size=16,face="plain", margin = margin(r=0,10,0,0)),
axis.title.y = element_text(size=16,face="plain", margin = margin(r=10,0,0,0)),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(.3, "cm"))+
scale_fill_manual(values=c(colpal[1],colpal[2],colpal[3],colpal[4]),name = "Density", labels = c("0.26",'0.52','0.67','1.33'))
plot1<-grid.arrange(BarPlot_1,BarPlot_2, nrow = 2,ncol=1)
Figure 13: Covariance components for variance decomposition in males (A)
and females(B)
sessionInfo()R version 4.2.0 (2022-04-22 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
Matrix products: default
locale:
[1] LC_COLLATE=German_Germany.utf8 LC_CTYPE=German_Germany.utf8
[3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C
[5] LC_TIME=German_Germany.utf8
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ICC_2.4.0 tidyr_1.2.0 data.table_1.14.2 boot_1.3-28
[5] RColorBrewer_1.1-3 car_3.1-0 carData_3.0-5 gridGraphics_0.5-1
[9] cowplot_1.1.1 EnvStats_2.7.0 dplyr_1.0.9 readr_2.1.2
[13] lmerTest_3.1-3 lme4_1.1-30 Matrix_1.4-1 gridExtra_2.3
[17] ggplot2_3.3.6 ggeffects_1.1.3 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] httr_1.4.3 sass_0.4.2 bit64_4.0.5
[4] vroom_1.5.7 jsonlite_1.8.0 splines_4.2.0
[7] bslib_0.4.0 getPass_0.2-2 highr_0.9
[10] yaml_2.3.5 numDeriv_2016.8-1.1 pillar_1.8.0
[13] lattice_0.20-45 glue_1.6.2 digest_0.6.29
[16] promises_1.2.0.1 minqa_1.2.4 colorspace_2.0-3
[19] htmltools_0.5.3 httpuv_1.6.5 pkgconfig_2.0.3
[22] purrr_0.3.4 scales_1.2.0 processx_3.7.0
[25] whisker_0.4 later_1.3.0 tzdb_0.3.0
[28] git2r_0.30.1 tibble_3.1.7 mgcv_1.8-40
[31] farver_2.1.1 generics_0.1.3 ellipsis_0.3.2
[34] cachem_1.0.6 withr_2.5.0 cli_3.3.0
[37] crayon_1.5.1 magrittr_2.0.3 evaluate_0.16
[40] ps_1.7.1 fs_1.5.2 fansi_1.0.3
[43] nlme_3.1-157 MASS_7.3-56 tools_4.2.0
[46] hms_1.1.1 lifecycle_1.0.1 stringr_1.4.0
[49] munsell_0.5.0 callr_3.7.1 compiler_4.2.0
[52] jquerylib_0.1.4 rlang_1.0.2 nloptr_2.0.3
[55] rstudioapi_0.13 labeling_0.4.2 rmarkdown_2.14
[58] gtable_0.3.0 abind_1.4-5 R6_2.5.1
[61] knitr_1.39 fastmap_1.1.0 bit_4.0.4
[64] utf8_1.2.2 rprojroot_2.0.3 stringi_1.7.8
[67] parallel_4.2.0 Rcpp_1.0.9 vctrs_0.4.1
[70] tidyselect_1.1.2 xfun_0.31