Covariance Correlations
Last updated: 2023-08-28
Checks: 6 1
Knit directory: dgrp-starve/
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ggTidy <- function(data){
for(i in 1:dim(data)[2]){
name <- colnames(data)[i]
temp <- cbind(rep(name, dim(data)[1]), data[,i, with=FALSE])
if(i==1){
hold <- temp
} else{
hold <- rbind(hold, temp, use.names=FALSE)
}
}
colnames(hold) <- c("method", "cor")
return(hold)
}
filt <- function(data, method){
if(method=="sr"){
col <- 1
return(unlist(data[col, ]))
} else if(method=="nested"){
hold <- vector(length=50)
for(i in 1:length(data)){
hold[i] <- data[[i]]$cor
}
return(hold)
} else if(method=='top3'){
col <- 1
start <- 1
skip <- 16
raw <- unlist(data[col, ])
trim <- raw[seq(start, length(raw), by=skip)]
return(trim)
} else{
print('Method not recognized.')
return(NA)
}
}The following methods are currently implemented:
Mr. Mash
GBLUP
BayesC
addNewData <- 1
if(addNewData){
#READ IN
raw_sr_rf <- readRDS("snake/data/sr/30_summary/rf_f.Rds")
raw_sr_pcr <- readRDS("snake/data/sr/30_summary/pcr_f.Rds")
raw_sr_varbvs <- readRDS("snake/data/20_cor/varbvs_f_starvation.Rds")
raw_sr_bayesC <- readRDS("snake/data/sr/30_summary/bayesC_0.9_f.Rds")
raw_top3_bayesC <- readRDS('snake/data/top3/30_summary/multibayesC_f_top3.Rds')
#raw_top3_gblup <- readRDS('snake/data/top3/30_summary/multibayesC_f.Rds')
raw_sr_gblup <- readRDS("snake/data/20_cor/gblup_f_starvation.Rds")
raw_top3_gblup <- readRDS('snake/data/top3/30_summary/multigblup_f_top3.Rds')
#raw_top3_gblup <- readRDS('snake/data/top3/30_summary/multigblup_f.Rds')
raw_sr_rr <- readRDS("snake/data/20_cor/rr_f_starvation.Rds")
raw_sr_lasso <- readRDS("snake/data/20_cor/lasso_f_starvation.Rds")
raw_top3_lasso <- readRDS("snake/data/top3/30_summary/mlasso_f.Rds")
#raw_top3_mash <- readRDS('snake/data/top3/30_summary/mash_f.Rds')
raw_top3_mash <- readRDS('snake/data/top3/30_summary/mr.mash_f_top3.Rds')
raw_top3_datamash <- readRDS('snake/data/top3/30_summary/datadrive_f.Rds')
raw_sr_nn <-readRDS('snake/data/sr/30_summary/nn_f.Rds')
raw_sr_pcr <- readRDS('snake/data/sr/30_summary/pcr_f.Rds')
#FILTER
sr_rf <- filt(raw_sr_rf, "sr")
sr_pcr <- filt(raw_sr_pcr, "sr")
sr_varbvs <- filt(raw_sr_varbvs, "nested")
sr_bayesC <- filt(raw_sr_bayesC, "sr")
top3_bayesC <- filt(raw_top3_bayesC, "top3")
sr_gblup <- filt(raw_sr_gblup, "nested")
top3_gblup <- filt(raw_top3_gblup, "top3")
sr_rr <- filt(raw_sr_rr, "nested")
sr_lasso <- filt(raw_sr_lasso, "nested")
top3_lasso <- filt(raw_top3_lasso, "top3")
top3_mash <- filt(raw_top3_mash, "top3")
top3_datamash <- filt(raw_top3_datamash, "top3")
sr_nn <- filt(raw_sr_nn, "sr")
sr_pcr <- filt(raw_sr_pcr, "sr")
allData <- data.table(sr_rf, sr_pcr, sr_nn, sr_varbvs, sr_bayesC,
top3_bayesC, sr_gblup, top3_gblup,
sr_rr, sr_lasso, top3_lasso, top3_mash,
top3_datamash)
saveRDS(allData, "data/hist_data_f.Rds")
}allData <- readRDS("data/hist_data_f.Rds")
print(colMeans(allData)) sr_rf sr_pcr sr_nn sr_varbvs sr_bayesC
0.36402243 0.45831216 -0.01710806 0.41300077 0.31800121
top3_bayesC sr_gblup top3_gblup sr_rr sr_lasso
0.25436812 0.27998224 0.30159686 0.28108963 0.34736423
top3_lasso top3_mash top3_datamash
0.18794887 0.09036488 0.13114167 data <- ggTidy(allData)
#graphing----
iter <- 50
data$method <- factor(data$method, levels=unique(data$method))
gg[[1]] <- ggplot(data, aes(x=method, y=cor, fill=method)) +
geom_violin(color = NA, width = 0.65) +
geom_boxplot(color='#440154FF', width = 0.15) +
theme_minimal() +
stat_summary(fun=mean, color='#440154FF', geom='point',
shape=18, size=3, show.legend=FALSE) +
labs(x=NULL,y='Correlation',tag='F') +
theme(legend.position='none',
axis.text.x = element_text(angle = -45, size=10),
text=element_text(size=10),
plot.tag = element_text(size=15)) +
scale_fill_viridis(begin = 0.4, end=0.9,discrete=TRUE)addNewData <- 1
if(addNewData){
#READ IN
raw_sr_rf <- readRDS("snake/data/sr/30_summary/rf_m.Rds")
raw_sr_pcr <- readRDS("snake/data/sr/30_summary/pcr_m.Rds")
raw_sr_varbvs <- readRDS("snake/data/20_cor/varbvs_m_starvation.Rds")
raw_sr_bayesC <- readRDS("snake/data/sr/30_summary/bayesC_0.9_m.Rds")
raw_top3_bayesC <- readRDS('snake/data/top3/30_summary/multibayesC_m_top3.Rds')
#raw_top3_gblup <- readRDS('snake/data/top3/30_summary/multibayesC_m.Rds')
raw_sr_gblup <- readRDS("snake/data/20_cor/gblup_m_starvation.Rds")
raw_top3_gblup <- readRDS('snake/data/top3/30_summary/multigblup_m_top3.Rds')
#raw_top3_gblup <- readRDS('snake/data/top3/30_summary/multigblup_m.Rds')
raw_sr_rr <- readRDS("snake/data/20_cor/rr_m_starvation.Rds")
raw_sr_lasso <- readRDS("snake/data/20_cor/lasso_m_starvation.Rds")
raw_top3_lasso <- readRDS("snake/data/top3/30_summary/mlasso_m.Rds")
#raw_top3_mash <- readRDS('snake/data/top3/30_summary/mash_m.Rds')
raw_top3_mash <- readRDS('snake/data/top3/30_summary/mr.mash_m_top3.Rds')
raw_top3_datamash <- readRDS('snake/data/top3/30_summary/datadrive_m.Rds')
raw_sr_nn <-readRDS('snake/data/sr/30_summary/nn_m.Rds')
raw_sr_pcr <- readRDS('snake/data/sr/30_summary/pcr_m.Rds')
#FILTER
sr_rf <- filt(raw_sr_rf, "sr")
sr_pcr <- filt(raw_sr_pcr, "sr")
sr_varbvs <- filt(raw_sr_varbvs, "nested")
sr_bayesC <- filt(raw_sr_bayesC, "sr")
top3_bayesC <- filt(raw_top3_bayesC, "top3")
sr_gblup <- filt(raw_sr_gblup, "nested")
top3_gblup <- filt(raw_top3_gblup, "top3")
sr_rr <- filt(raw_sr_rr, "nested")
sr_lasso <- filt(raw_sr_lasso, "nested")
top3_lasso <- filt(raw_top3_lasso, "top3")
top3_mash <- filt(raw_top3_mash, "top3")
top3_datamash <- filt(raw_top3_datamash, "top3")
sr_nn <- filt(raw_sr_nn, "sr")
allData <- data.table(sr_rf, sr_pcr, sr_nn, sr_varbvs, sr_bayesC,
top3_bayesC, sr_gblup, top3_gblup,
sr_rr, sr_lasso, top3_lasso, top3_mash,
top3_datamash)
saveRDS(allData, "data/hist_data_m.Rds")
}allData <- readRDS("data/hist_data_m.Rds")
print(colMeans(allData)) sr_rf sr_pcr sr_nn sr_varbvs sr_bayesC
0.381755740 0.528333785 -0.009744473 0.255439602 0.431298902
top3_bayesC sr_gblup top3_gblup sr_rr sr_lasso
0.352033267 0.349139906 0.365246159 0.362573999 0.246061992
top3_lasso top3_mash top3_datamash
0.090795620 0.252217985 0.276341392 data <- ggTidy(allData)
#graphing----
iter <- 50
data$method <- factor(data$method, levels=unique(data$method))
gg[[2]] <- ggplot(data, aes(x=method, y=cor, fill=method)) +
geom_violin(color = NA, width = 0.65) +
geom_boxplot(color='#440154FF', width = 0.15) +
theme_minimal() +
stat_summary(fun=mean, color='#440154FF', geom='point',
shape=18, size=3, show.legend=FALSE) +
labs(x=NULL,y='Correlation',tag='F') +
theme(legend.position='none',
axis.text.x = element_text(angle = -45, size=10),
text=element_text(size=10),
plot.tag = element_text(size=15)) +
scale_fill_viridis(begin = 0.4, end=0.9,discrete=TRUE)Female Correlation
plot_grid(gg[[1]], ncol=1)
Male Correlation
plot_grid(gg[[2]], ncol=1)
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Rocky Linux 8.5 (Green Obsidian)
Matrix products: default
BLAS/LAPACK: /opt/ohpc/pub/libs/gnu9/openblas/0.3.7/lib/libopenblasp-r0.3.7.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] reshape2_1.4.4 melt_1.10.0 ggcorrplot_0.1.4 lubridate_1.9.2
[5] forcats_1.0.0 stringr_1.5.0 purrr_1.0.1 readr_2.1.4
[9] tidyr_1.3.0 tibble_3.2.1 tidyverse_2.0.0 scales_1.2.1
[13] viridis_0.6.2 viridisLite_0.4.2 qqman_0.1.8 cowplot_1.1.1
[17] ggplot2_3.4.2 data.table_1.14.8 dplyr_1.1.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.11 getPass_0.2-2 ps_1.7.5 rprojroot_2.0.3
[5] digest_0.6.33 utf8_1.2.3 plyr_1.8.8 R6_2.5.1
[9] evaluate_0.21 httr_1.4.5 highr_0.10 pillar_1.9.0
[13] rlang_1.1.1 rstudioapi_0.15.0 whisker_0.4.1 callr_3.7.3
[17] jquerylib_0.1.4 rmarkdown_2.23 labeling_0.4.2 munsell_0.5.0
[21] compiler_4.1.2 httpuv_1.6.9 xfun_0.39 pkgconfig_2.0.3
[25] htmltools_0.5.5 tidyselect_1.2.0 gridExtra_2.3 fansi_1.0.4
[29] calibrate_1.7.7 tzdb_0.3.0 withr_2.5.0 later_1.3.1
[33] MASS_7.3-60 grid_4.1.2 jsonlite_1.8.7 gtable_0.3.3
[37] lifecycle_1.0.3 git2r_0.31.0 magrittr_2.0.3 cli_3.6.1
[41] stringi_1.7.12 cachem_1.0.8 farver_2.1.1 fs_1.6.3
[45] promises_1.2.0.1 bslib_0.5.0 generics_0.1.3 vctrs_0.6.2
[49] tools_4.1.2 glue_1.6.2 hms_1.1.3 processx_3.8.2
[53] fastmap_1.1.1 yaml_2.3.7 timechange_0.2.0 colorspace_2.1-0
[57] knitr_1.43 sass_0.4.7