Results
2021-June-07
Last updated: 2021-07-14
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Knit directory: implementGMSinCassava/
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| html | cc1eb4b | wolfemd | 2021-07-14 | Build site. |
| Rmd | 772750a | wolfemd | 2021-07-14 | DirDom model and selection index calc fully integrated functions. |
Raw data
Summary of the number of unique plots, locations, years, etc. in the cleaned plot-basis data. See here for details..
library(tidyverse); library(magrittr); library(ragg)
rawdata<-readRDS(file=here::here("output","IITA_ExptDesignsDetected_2021May10.rds"))
rawdata %>%
summarise(Nplots=nrow(.),
across(c(locationName,studyYear,studyName,TrialType,GID), ~length(unique(.)),.names = "N_{.col}")) %>%
rmarkdown::paged_table()This is not the same number of clones as are expected to be genotyped-and-phenotyped.
Break down the plots based on the trial design and TrialType (really a grouping of the population that is breeding program specific), captured by two logical variables, CompleteBlocks and IncompleteBlocks.
rawdata %>%
count(TrialType,CompleteBlocks,IncompleteBlocks) %>%
spread(TrialType,n) %>%
rmarkdown::paged_table()Next, look at breakdown of plots by TrialType (rows) and locations (columns):
rawdata %>%
count(locationName,TrialType) %>%
spread(locationName,n) %>%
rmarkdown::paged_table()traits<-c("MCMDS","DM","PLTHT","BRNHT1","BRLVLS","HI",
"logDYLD", # <-- logDYLD now included.
"logFYLD","logTOPYLD","logRTNO","TCHART","LCHROMO","ACHROMO","BCHROMO")
rawdata %>%
select(locationName,studyYear,studyName,TrialType,any_of(traits)) %>%
pivot_longer(cols = any_of(traits), values_to = "Value", names_to = "Trait") %>%
ggplot(.,aes(x=Value,fill=Trait)) + geom_histogram() + facet_wrap(~Trait, scales='free') +
theme_bw() + scale_fill_viridis_d() +
labs(title = "Distribution of Raw Phenotypic Values")
| Version | Author | Date |
|---|---|---|
| cc1eb4b | wolfemd | 2021-07-14 |
How many genotyped-and-phenotyped clones?
rawdata %>%
select(locationName,studyYear,studyName,TrialType,germplasmName,FullSampleName,GID,any_of(traits)) %>%
pivot_longer(cols = any_of(traits), values_to = "Value", names_to = "Trait") %>%
filter(!is.na(Value),!is.na(FullSampleName)) %>%
distinct(germplasmName,FullSampleName,GID) %>%
rmarkdown::paged_table()There are 8149 genotyped-and-phenotyped clones!
BLUPs
These are the BLUPs combining data for each clone across trials/locations without genomic information, used as input for genomic prediction downstream.
blups<-readRDS(file=here::here("data","blups_forCrossVal.rds"))
gidWithBLUPs<-blups %>% select(Trait,blups) %>% unnest(blups) %$% unique(GID)
rawdata %>%
select(observationUnitDbId,GID,any_of(blups$Trait)) %>%
pivot_longer(cols = any_of(blups$Trait),
names_to = "Trait",
values_to = "Value",values_drop_na = T) %>%
filter(GID %in% gidWithBLUPs) %>%
group_by(Trait) %>%
summarize(Nplots=n()) %>%
ungroup() %>%
left_join(blups %>%
mutate(Nclones=map_dbl(blups,~nrow(.)),
avgREL=map_dbl(blups,~mean(.$REL)),
Vg=map_dbl(varcomp,~.["GID!GID.var","component"]),
Ve=map_dbl(varcomp,~.["R!variance","component"]),
H2=Vg/(Vg+Ve)) %>%
select(-blups,-varcomp)) %>%
mutate(across(is.numeric,~round(.,3))) %>% arrange(desc(H2)) %>%
rmarkdown::paged_table()blups %>%
select(Trait,blups) %>%
unnest(blups) %>%
ggplot(.,aes(x=drgBLUP,fill=Trait)) + geom_histogram() + facet_wrap(~Trait, scales='free') +
theme_bw() + scale_fill_viridis_d() + theme(legend.position = 'none') +
labs(title = "Distribution of de-regressed BLUP Values")
| Version | Author | Date |
|---|---|---|
| cc1eb4b | wolfemd | 2021-07-14 |
blups %>%
select(Trait,blups) %>%
unnest(blups) %>%
ggplot(.,aes(x=Trait,y=REL,fill=Trait)) + geom_boxplot(notch=T) + #facet_wrap(~Trait, scales='free') +
theme_bw() + scale_fill_viridis_d() +
theme(axis.text.x = element_text(angle=90),
legend.position = 'none') +
labs(title = "Distribution of BLUP Reliabilities")
| Version | Author | Date |
|---|---|---|
| cc1eb4b | wolfemd | 2021-07-14 |
Marker density and distribution
library(tidyverse); library(magrittr);
snps<-readRDS(file=here::here("data","dosages_IITA_filtered_2021May13.rds"))
mrks<-colnames(snps) %>%
tibble(SNP_ID=.) %>%
separate(SNP_ID,c("Chr","Pos","Allele"),"_") %>%
mutate(Chr=as.integer(gsub("S","",Chr)),
Pos=as.numeric(Pos))
mrks %>%
ggplot(.,aes(x=Pos,fill=as.character(Chr))) + geom_histogram() +
facet_wrap(~Chr,scales = 'free') + theme_bw() +
scale_fill_viridis_d() + theme(legend.position = 'none',
axis.text.x = element_text(angle=90))
| Version | Author | Date |
|---|---|---|
| cc1eb4b | wolfemd | 2021-07-14 |
mrks %>% count(Chr) %>% rmarkdown::paged_table()Pedigree
Summarize the pedigree and the verification results described here.
library(tidyverse); library(magrittr);
ped2check_genome<-readRDS(file=here::here("output","ped2check_genome.rds"))
ped2check_genome %<>%
select(IID1,IID2,Z0,Z1,Z2,PI_HAT)
ped2check<-read.table(file=here::here("output","ped2genos.txt"),
header = F, stringsAsFactors = F) %>%
rename(FullSampleName=V1,DamID=V2,SireID=V3)
ped2check %<>%
select(FullSampleName,DamID,SireID) %>%
inner_join(ped2check_genome %>%
rename(FullSampleName=IID1,DamID=IID2) %>%
bind_rows(ped2check_genome %>%
rename(FullSampleName=IID2,DamID=IID1))) %>%
distinct %>%
mutate(FemaleParent=case_when(Z0<0.32 & Z1>0.67~"Confirm",
SireID==DamID & PI_HAT>0.6 & Z0<0.3 & Z2>0.32~"Confirm",
TRUE~"Reject")) %>%
select(-Z0,-Z1,-Z2,-PI_HAT) %>%
inner_join(ped2check_genome %>%
rename(FullSampleName=IID1,SireID=IID2) %>%
bind_rows(ped2check_genome %>%
rename(FullSampleName=IID2,SireID=IID1))) %>%
distinct %>%
mutate(MaleParent=case_when(Z0<0.32 & Z1>0.67~"Confirm",
SireID==DamID & PI_HAT>0.6 & Z0<0.3 & Z2>0.32~"Confirm",
TRUE~"Reject")) %>%
select(-Z0,-Z1,-Z2,-PI_HAT)
rm(ped2check_genome)
ped2check %<>%
mutate(Cohort=NA,
Cohort=ifelse(grepl("TMS18",FullSampleName,ignore.case = T),"TMS18",
ifelse(grepl("TMS15",FullSampleName,ignore.case = T),"TMS15",
ifelse(grepl("TMS14",FullSampleName,ignore.case = T),"TMS14",
ifelse(grepl("TMS13|2013_",FullSampleName,ignore.case = T),"TMS13","GGetc")))))Proportion of accessions with male, female or both parents in pedigree confirm-vs-rejected?
ped2check %>%
count(FemaleParent,MaleParent) %>%
mutate(Prop=round(n/sum(n),2)) FemaleParent MaleParent n Prop
1 Confirm Confirm 4259 0.77
2 Confirm Reject 563 0.10
3 Reject Confirm 382 0.07
4 Reject Reject 313 0.06Proportion of accessions within each Cohort with pedigree records confirmed-vs-rejected?
ped2check %>%
count(Cohort,FemaleParent,MaleParent) %>%
spread(Cohort,n) %>%
mutate(across(is.numeric,~round(./sum(.),2))) %>%
rmarkdown::paged_table()Use only fully-confirmed families / trios. Remove any without both parents confirmed.
ped<-read.table(here::here("output","verified_ped.txt"),
header = T, stringsAsFactors = F) %>%
mutate(Cohort=NA,
Cohort=ifelse(grepl("TMS18",FullSampleName,ignore.case = T),"TMS18",
ifelse(grepl("TMS15",FullSampleName,ignore.case = T),"TMS15",
ifelse(grepl("TMS14",FullSampleName,ignore.case = T),"TMS14",
ifelse(grepl("TMS13|2013_",FullSampleName,ignore.case = T),"TMS13","GGetc")))))Summary of family sizes
ped %>%
count(SireID,DamID) %$% summary(n) Min. 1st Qu. Median Mean 3rd Qu. Max.
1.00 1.00 3.00 5.85 8.00 77.00 ped %>% nrow(.) # 4259 pedigree entries[1] 4259ped %>%
count(Cohort,name = "Number of Verified Pedigree Entries") Cohort Number of Verified Pedigree Entries
1 GGetc 18
2 TMS13 1786
3 TMS14 1302
4 TMS15 589
5 TMS18 564ped %>%
distinct(Cohort,SireID,DamID) %>%
count(Cohort,name = "Number of Families per Cohort") Cohort Number of Families per Cohort
1 GGetc 16
2 TMS13 120
3 TMS14 233
4 TMS15 197
5 TMS18 164730 families. Mean size 5.85, range 1-77.
Parent-wise Cross-validation
parentfolds<-readRDS(file=here::here("output","parentfolds.rds"))
summarized_parentfolds<-parentfolds %>%
mutate(Ntestparents=map_dbl(testparents,length),
Ntrainset=map_dbl(trainset,length),
Ntestset=map_dbl(testset,length),
NcrossesToPredict=map_dbl(CrossesToPredict,nrow)) %>%
select(Repeat,Fold,starts_with("N"))
summarized_parentfolds %>%
rmarkdown::paged_table()summarized_parentfolds %>% summarize(across(is.numeric,median,.names = "median{.col}"))# A tibble: 1 x 4
medianNtestparents medianNtrainset medianNtestset medianNcrossesToPredict
<dbl> <dbl> <dbl> <dbl>
1 55 2053 2125 195Selection index weights
c(logFYLD=20,
HI=10,
DM=15,
MCMDS=-10,
logRTNO=12,
logDYLD=20,
logTOPYLD=15,
PLTHT=10) logFYLD HI DM MCMDS logRTNO logDYLD logTOPYLD PLTHT
20 10 15 -10 12 20 15 10 Prediction accuracy
- Check prediction accuracy: Evaluate prediction accuracy with cross-validation.
Selection Index Accuracy
Selection index weights
# SELECTION INDEX WEIGHTS
## from IYR+IK
## note that not ALL predicted traits are on index
SIwts<-c(logFYLD=20,
HI=10,
DM=15,
MCMDS=-10,
logRTNO=12,
logDYLD=20,
logTOPYLD=15,
PLTHT=10)
SIwts logFYLD HI DM MCMDS logRTNO logDYLD logTOPYLD PLTHT
20 10 15 -10 12 20 15 10 library(ggdist)
accuracy_ad<-readRDS(here::here("output","cvAD_5rep5fold_predAccuracy.rds"))
accuracy_dirdom<-readRDS(here::here("output","cvDirDom_5rep5fold_predAccuracy.rds"))
accuracy<-accuracy_ad$meanPredAccuracy %>%
bind_rows(accuracy_dirdom$meanPredAccuracy) %>%
filter(Trait=="SELIND") %>%
mutate(VarComp=gsub("Mean","",predOf),
predOf="Mean") %>%
bind_rows(accuracy_ad$varPredAccuracy %>%
bind_rows(accuracy_dirdom$varPredAccuracy) %>%
filter(Trait1=="SELIND") %>%
rename(Trait=Trait1) %>%
select(-Trait2) %>%
mutate(VarComp=gsub("Var","",predOf),
predOf="Var")) %>%
select(-predVSobs)
colors<-viridis::viridis(4)[1:2]accuracy %>%
mutate(predOf=factor(predOf,levels=c("Mean","Var")),
VarComp=factor(VarComp,levels=c("BV","TGV"))) %>%
ggplot(.,aes(x=VarComp,y=AccuracyEst,fill=VarComp)) +
ggdist::stat_halfeye(adjust=0.5,.width = 0,fill='gray',width=0.75) +
geom_boxplot(width=0.12,notch = TRUE) +
ggdist::stat_dots(side = "left",justification = 1.1,
binwidth = 0.03,dotsize=0.6) +
theme_bw() +
scale_fill_manual(values = colors) +
geom_hline(yintercept = 0, color='black', size=0.9) +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
strip.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title = element_text(face='bold',color = 'black'),
strip.text.x = element_text(face='bold',color='black',size=14),
axis.text.y = element_text(face = 'bold',color='black'),
legend.text = element_text(face='bold'),
legend.position = 'bottom') +
facet_grid(predOf~modelType,scales = 'free') +
#facet_wrap(~predOf+modelType,scales = 'free_y',nrow=1) +
labs(title="Selection Index Prediction Accuracy") +
coord_cartesian(xlim = c(1.2, NA))
| Version | Author | Date |
|---|---|---|
| cc1eb4b | wolfemd | 2021-07-14 |
Accuracy predicting cross-means
accuracy_ad$meanPredAccuracy %>%
bind_rows(accuracy_dirdom$meanPredAccuracy) %>%
select(-predVSobs) %>%
mutate(Trait=factor(Trait,levels=c("SELIND",blups$Trait)),
predOf=factor(paste0(predOf,"_",modelType),levels=c("MeanBV_AD","MeanTGV_AD","MeanBV_DirDom","MeanTGV_DirDom"))) %>%
ggplot(.,aes(x=predOf,y=AccuracyEst,fill=predOf,color=modelType)) +
geom_boxplot(width=0.4,notch = TRUE, color='gray40') +
ggdist::stat_dots(side = "left", justification = 1.3,
binwidth = 0.03,dotsize=0.5,layout="swarm") +
scale_fill_manual(values = viridis::viridis(4)[1:4]) +
scale_color_manual(values = viridis::viridis(4)[1:4]) +
geom_hline(yintercept = 0, color='black', size=0.8) +
facet_grid(.~Trait) +
labs(title="Accuracy predicting cross-means") +
theme_bw() +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
strip.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.y = element_text(face='bold'),
strip.text.x = element_text(face='bold'),
legend.position='bottom',
legend.text = element_text(face='bold'),
panel.spacing = unit(0.05, "lines"))
| Version | Author | Date |
|---|---|---|
| cc1eb4b | wolfemd | 2021-07-14 |
Accuracy predicting variance params
accuracy_ad$varPredAccuracy %>%
bind_rows(accuracy_dirdom$varPredAccuracy) %>%
select(-predVSobs) %>%
filter(Trait1==Trait2) %>%
mutate(Trait1=factor(Trait1,levels=c("SELIND",blups$Trait)),
predOf=factor(paste0(predOf,"_",modelType),
levels=c("VarBV_AD","VarTGV_AD",
"VarBV_DirDom","VarTGV_DirDom"))) %>%
ggplot(.,aes(x=predOf,y=AccuracyEst,fill=predOf,color=modelType)) +
geom_boxplot(width=0.4,notch = TRUE,color='gray40') +
ggdist::stat_dots(side = "left", justification = 1.3,layout='swarm',
binwidth = 0.03,dotsize=0.4) +
scale_fill_manual(values = viridis::viridis(4)[1:4]) +
scale_color_manual(values = viridis::viridis(4)[1:4]) +
geom_hline(yintercept = 0, color='black', size=0.8) +
facet_grid(.~Trait1) +
labs(title="Accuracy predicting cross-variances") +
theme_bw() +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
strip.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.y = element_text(face='bold'),
strip.text.x = element_text(face='bold'),
legend.position='bottom',
legend.text = element_text(face='bold'),
panel.spacing = unit(0.05, "lines"))
| Version | Author | Date |
|---|---|---|
| cc1eb4b | wolfemd | 2021-07-14 |
accuracy_ad$varPredAccuracy %>%
bind_rows(accuracy_dirdom$varPredAccuracy) %>%
select(-predVSobs) %>%
filter(Trait1!="SELIND",Trait2!="SELIND",
Trait1!=Trait2) %>%
mutate(#Trait1=factor(Trait1,levels=c("SELIND",blups$Trait)),
#Trait2=factor(Trait2,levels=c("SELIND",blups$Trait)),
Trait1=factor(Trait1,levels=c(blups$Trait)),
Trait2=factor(Trait2,levels=c(blups$Trait)),
predOf=factor(paste0(predOf,"_",modelType),
levels=c("VarBV_AD","VarTGV_AD",
"VarBV_DirDom","VarTGV_DirDom"))) %>%
ggplot(.,aes(x=predOf,y=AccuracyEst,fill=predOf,color=modelType)) +
geom_boxplot(notch = TRUE) +
scale_fill_manual(values = viridis::viridis(4)[1:4]) +
scale_color_manual(values = viridis::viridis(4)[1:4]) +
geom_hline(yintercept = 0, color='gray40', size=0.6) +
facet_wrap(~Trait1+Trait2,nrow=5) +
labs(title="Accuracy predicting cross-covariances") +
theme_bw() +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
strip.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.y = element_text(face='bold'),
strip.text.x = element_text(size=8),
strip.text.y = element_text(size=8,angle = 0),
legend.position='bottom',
legend.text = element_text(face='bold'),
panel.spacing = unit(0.05, "lines"))
| Version | Author | Date |
|---|---|---|
| cc1eb4b | wolfemd | 2021-07-14 |
[TO REDO] Accuracy based only on large families
What if we only compute accuracy with families having >10 or >20 members?
# library(tidyverse); library(magrittr)
# cvvars<-readRDS(here::here("output","cvVarPredAccuracyAD.rds"))
#
# big_acc<-cvvars %>%
# mutate(AccuracyEst_n10=map_dbl(predVSobs,function(predVSobs){
# z<-predVSobs %>% filter(famSize>=10)
# out<-psych::cor.wt(z[,c("predVar","obsVar")],
# w = z$famSize) %$% r[1,2] %>%
# round(.,3)
# return(out) }),
# AccuracyEst_n20=map_dbl(predVSobs,function(predVSobs){
# z<-predVSobs %>% filter(famSize>=20)
# out<-psych::cor.wt(z[,c("predVar","obsVar")],
# w = z$famSize) %$% r[1,2] %>%
# round(.,3)
# return(out) }))
#
# big_acc %<>%
# mutate(Trait1=factor(Trait1,levels=c("SELIND",blups$Trait)),
# Trait2=factor(Trait2,levels=c("SELIND",blups$Trait)),
# predOf=factor(predOf,levels=c("VarBV","VarTGV")))
#
# big_acc %>%
# filter(Trait1==Trait2,predOf=="VarBV") %>%
# select(-predVSobs) %>%
# pivot_longer(cols = contains("Accuracy"), names_to = "FamilySizeForValidation",values_to = "AccuracyEst") %>%
# ggplot(.,aes(x=predOf,y=AccuracyEst,fill=FamilySizeForValidation, color=predOf)) +
# geom_boxplot(width=0.4,notch = TRUE,color='gray40') +
# scale_fill_manual(values = viridis::viridis(4)[1:3]) +
# #scale_color_manual(values = viridis::viridis(4)[1:3]) +
# geom_hline(yintercept = 0, color='black', size=0.8) +
# facet_grid(predOf~Trait1) +
# labs(title="Accuracy predicting cross-variances in bigger famiilies only?") +
# theme_bw() +
# theme(axis.text.x = element_blank(),
# axis.title.x = element_blank(),
# strip.background = element_blank(),
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),
# axis.text.y = element_text(face='bold'),
# strip.text.x = element_text(face='bold'),
# legend.position='bottom',
# legend.text = element_text(face='bold'),
# panel.spacing = unit(0.05, "lines"))[RUNNING] Genomic Mate Selection
Placeholder for summaries of the full-model predictions of cross means and variances to-be-used for selection.
Genetic Gain Estimates
# GBLUPs
### Two models AD and DirDom
#gpreds_ad<-readRDS(file = here::here("output","genomicPredictions_ModelAD.rds"))
gpreds_dirdom<-readRDS(file = here::here("output","genomicPredictions_ModelDirDom.rds"))
si_getgvs<-gpreds_dirdom$gblups[[1]] %>%
#si_getgvs<-gpreds_ad$gblups[[1]] %>%
filter(predOf=="GETGV") %>%
select(GID,SELIND)
## IITA Germplasm Ages
ggcycletime<-readxl::read_xls(here::here("data","PedigreeGeneticGainCycleTime_aafolabi_01122020.xls")) %>%
mutate(Year_Accession=as.numeric(Year_Accession))
# Need germplasmName field from raw trial data to match GEBV and cycle time
rawdata<-readRDS(here::here("output","IITA_ExptDesignsDetected_2021May10.rds"))
si_getgvs %<>%
left_join(rawdata %>%
distinct(germplasmName,GID)) %>%
group_by(GID) %>%
slice(1) %>%
ungroup()
# table(ggcycletime$Accession %in% si_getgvs$germplasmName)
# FALSE TRUE
# 193 614
si_getgvs %<>%
left_join(.,ggcycletime %>%
rename(germplasmName=Accession) %>%
mutate(Year_Accession=as.numeric(Year_Accession))) %>%
mutate(Year_Accession=case_when(grepl("2013_|TMS13",germplasmName)~2013,
grepl("TMS14",germplasmName)~2014,
grepl("TMS15",germplasmName)~2015,
grepl("TMS18",germplasmName)~2018,
!grepl("2013_|TMS13|TMS14|TMS15|TMS18",germplasmName)~Year_Accession))
# Declare the "eras" as PreGS\<2012 and GS\>=2013.
si_getgvs %<>%
filter(Year_Accession>2012 | Year_Accession<2005)
si_getgvs %<>%
mutate(GeneticGroup=ifelse(Year_Accession>=2013,"GS","PreGS"))Show rate of genetic gain - getgv vs. accession age
# , fig.height=10, fig.width=12
si_getgvs %>%
select(GeneticGroup,GID,Year_Accession,SELIND) %>%
ggplot(.,aes(x=Year_Accession,y=SELIND,color=GeneticGroup)) +
geom_point(size=1.25) + geom_smooth(method=lm, se=TRUE, size=1.5) +
# facet_wrap(~Trait,scales='free_y', ncol=2) +
theme_bw() +
theme(axis.text = element_text(face = 'bold',angle = 0, size=14),
axis.title = element_text(face = 'bold',size=16),
strip.background.x = element_blank(),
strip.text = element_text(face='bold',size=18)) +
scale_color_viridis_d() +
labs(title = "Selection Index GETGV vs. Accession Age by 'era' [GS vs. PreGS]",
subtitle = "SI GETGV from modelType='DirDom'")
| Version | Author | Date |
|---|---|---|
| cc1eb4b | wolfemd | 2021-07-14 |
Barplot of mean GEBV vs. Cycle
si_gebvs<-gpreds_dirdom$gblups[[1]] %>%
filter(predOf=="GEBV") %>%
select(GID,SELIND) %>%
mutate(GeneticGroup=case_when(grepl("2013_|TMS13",GID)~"C1",
grepl("TMS14",GID)~"C2",
grepl("TMS15",GID)~"C3",
grepl("TMS18",GID)~"C4",
!grepl("2013_|TMS13|TMS14|TMS15|TMS18",GID)~"PreGS"),
GeneticGroup=factor(GeneticGroup,levels = c("PreGS","C1","C2","C3","C4")))
si_gebvs %>%
group_by(GeneticGroup) %>%
summarize(meanGEBV=mean(SELIND),
stdErr=sd(SELIND)/sqrt(n()),
upperSE=meanGEBV+stdErr,
lowerSE=meanGEBV-stdErr) %>%
ggplot(.,aes(x=GeneticGroup,y=meanGEBV,fill=GeneticGroup)) +
geom_bar(stat = 'identity', color='gray60', size=1.25) +
geom_linerange(aes(ymax=upperSE,
ymin=lowerSE), color='gray60', size=1.25) +
#facet_wrap(~Trait,scales='free_y', ncol=1) +
theme_bw() +
geom_hline(yintercept = 0, size=1.15, color='black') +
theme(axis.text.x = element_text(face = 'bold',angle = 0, size=12),
axis.title.y = element_text(face = 'bold',size=14),
legend.position = 'none',
strip.background.x = element_blank(),
strip.text = element_text(face='bold',size=14)) +
scale_fill_viridis_d() +
labs(x=NULL,y="Mean GEBVs",
title="Mean +/- Std. Error Selection Index GEBV by Cycle")
| Version | Author | Date |
|---|---|---|
| cc1eb4b | wolfemd | 2021-07-14 |
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggdist_2.4.1 ragg_1.1.3 magrittr_2.0.1 forcats_0.5.1
[5] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_1.4.0
[9] tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.5 tidyverse_1.3.1
[13] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] viridis_0.6.1 httr_1.4.2 sass_0.4.0
[4] jsonlite_1.7.2 viridisLite_0.4.0 splines_4.1.0
[7] here_1.0.1 modelr_0.1.8 bslib_0.2.5.1
[10] assertthat_0.2.1 distributional_0.2.2 highr_0.9
[13] cellranger_1.1.0 yaml_2.2.1 lattice_0.20-44
[16] pillar_1.6.1 backports_1.2.1 glue_1.4.2
[19] digest_0.6.27 promises_1.2.0.1 rvest_1.0.0
[22] colorspace_2.0-2 Matrix_1.3-4 htmltools_0.5.1.1
[25] httpuv_1.6.1 pkgconfig_2.0.3 broom_0.7.8
[28] haven_2.4.1 scales_1.1.1 whisker_0.4
[31] later_1.2.0 git2r_0.28.0 mgcv_1.8-36
[34] generics_0.1.0 farver_2.1.0 ellipsis_0.3.2
[37] withr_2.4.2 cli_3.0.0 crayon_1.4.1
[40] readxl_1.3.1 evaluate_0.14 fs_1.5.0
[43] fansi_0.5.0 nlme_3.1-152 xml2_1.3.2
[46] beeswarm_0.4.0 textshaping_0.3.5 tools_4.1.0
[49] hms_1.1.0 lifecycle_1.0.0 munsell_0.5.0
[52] reprex_2.0.0 compiler_4.1.0 jquerylib_0.1.4
[55] systemfonts_1.0.2 rlang_0.4.11 grid_4.1.0
[58] rstudioapi_0.13 labeling_0.4.2 rmarkdown_2.9
[61] gtable_0.3.0 DBI_1.1.1 R6_2.5.0
[64] gridExtra_2.3 lubridate_1.7.10 knitr_1.33
[67] utf8_1.2.1 rprojroot_2.0.2 stringi_1.6.2
[70] Rcpp_1.0.7 vctrs_0.3.8 dbplyr_2.1.1
[73] tidyselect_1.1.1 xfun_0.24