GO-BayesC R2 Comparison to GO-TBLUP
Last updated: 2024-03-26
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Knit directory: dgrp-starve/
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#plotmaker functions----
ggMake2 <- function(data, sex, yint, psize, custom.title, custom.Xlab, custom.Ylab, scaleStart, scaleEnd){
plothole <- ggplot(data,aes(y=cor,x=method, fill=typeRank))+
geom_hline(yintercept = yint)+
labs(x=custom.Xlab,y=custom.Ylab, tag=sex, title=custom.title, fill='Method Type') +
geom_violin(color = NA, width = 0.65) +
geom_boxplot(color='#440154FF', width = 0.15) +
theme_minimal()+
theme(axis.text.x = element_text(angle = 90),
text=element_text(size=5),
plot.tag = element_text(size=10)) +
scale_fill_viridis(begin = scaleStart, end = scaleEnd, discrete=TRUE, option='turbo',
labels=c('Transformation', 'Penalized', 'Bayesian', 'Mixed Model', 'Machine Learning'))+#false discrete for number ranked layover, true normally
stat_summary(fun=mean, color='#440154FF', geom='point',
shape=18, size=3, show.legend=FALSE)
return(plothole)
}
ggMakeVar <- function(data, sex, yint, psize, custom.title, custom.Xlab, custom.Ylab, scaleStart, scaleEnd){
plothole <- ggplot(data,aes(y=cor,x=method, fill=varSelect))+
geom_hline(yintercept = yint)+
labs(x=custom.Xlab,y=custom.Ylab, tag=sex, title=custom.title, fill='Method Type') +
geom_violin(color = NA, width = 0.65) +
geom_boxplot(color='#440154FF', width = 0.15) +
theme_minimal()+
theme(axis.text.x = element_text(angle = 90),
text=element_text(size=5),
plot.tag = element_text(size=10)) +
scale_fill_viridis(begin = scaleStart, end = scaleEnd, discrete=TRUE, option='turbo',
labels=c('Shrinkage', 'Shrinkage + Selection'))+#false discrete for number ranked layover, true normally
stat_summary(fun=mean, color='#440154FF', geom='point',
shape=18, size=3, show.legend=FALSE)
return(plothole)
}load('data/rmdTables/srPlots/allTables.Rdata')
gg[[1]] <- ggMake2(dataF, 'F', 0, 1, 'Prediction Accuracy by Method in Females', 'Method', 'Prediction Accuracy', 0.1, 0.9)
gg[[2]] <- ggMake2(dataM, 'M', 0, 1, 'Prediction Accuracy by Method in Males', 'Method', 'Prediction Accuracy', 0.1, 0.9)
gg[[3]] <- ggMakeVar(dataF, 'F', 0, 1, 'Prediction Accuracy by Variable Selection in Females', 'Method', 'Prediction Accuracy', 0.2, 0.8)
gg[[4]] <- ggMakeVar(dataM, 'M', 0, 1, 'Prediction Accuracy by Variable Selection in Males', 'Method', 'Prediction Accuracy', 0.2, 0.8)Rundown
Updated Prediction Accuracy Plots
plot_grid(gg[[1]], ncol = 1)
| Version | Author | Date |
|---|---|---|
| 4587511 | nklimko | 2024-03-26 |
plot_grid(gg[[2]], ncol = 1)
The 2 plots above are updated versions of the original
Variable Selection Marking
plot_grid(gg[[3]], ncol = 1)
| Version | Author | Date |
|---|---|---|
| 4587511 | nklimko | 2024-03-26 |
plot_grid(gg[[4]], ncol = 1)
Variable selection has a positive effect on prediction accuracy in females and a negative effect in males.
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_0.1.4.1 lubridate_1.9.3
[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_0.6.4 viridisLite_0.4.2 qqman_0.1.9 cowplot_1.1.1
[17] ggplot2_3.4.4 data.table_1.14.8 dplyr_1.1.3 workflowr_1.7.1
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_1.8.9 R6_2.5.1
[9] evaluate_0.21 highr_0.10 httr_1.4.7 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_0.4.3 munsell_0.5.0
[21] compiler_4.1.2 httpuv_1.6.12 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_0.4.0 withr_2.5.0 later_1.3.1
[33] MASS_7.3-60 grid_4.1.2 jsonlite_1.8.7 gtable_0.3.4
[37] lifecycle_1.0.3 git2r_0.32.0 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_0.6.4
[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