Last updated: 2021-08-11
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Knit directory: IITA_2021GS/
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Unstaged changes:
Modified: analysis/07-Results.Rmd
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Deleted: code/gsFunctions.R
Deleted: code/parentWiseCrossVal.R
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Deleted: output/cvDirDom_5rep5fold_predAccuracy.rds
Deleted: output/estimateSelectionError.rds
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/02-GetBLUPs.Rmd) and HTML (docs/02-GetBLUPs.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | e4df79f | wolfemd | 2021-08-11 | Completed IITA_2021GS pipeline including imputation and genomic prediction. Last bit of cross-validation and cross-prediction finishes in 24 hrs. |
| html | 934141c | wolfemd | 2021-07-14 | Build site. |
| Rmd | 2953ee6 | wolfemd | 2021-07-14 | Correct the link to the Results.html page throughout. |
| html | e66bdad | wolfemd | 2021-06-10 | Build site. |
| Rmd | a8452ba | wolfemd | 2021-06-10 | Initial build of the entire page upon completion of all |
Two-stage procedure:
Work below represents Stage 1 of the Two-stage procedure.
To fit the mixed-model I used last year, I am again resorting to asreml. I fit random effects for rep and block only where complete and incomplete blocks, respectively are indicated in the trial design variables. sommer should be able to fit the same model via the at() function, but I am having trouble with it’s memory intnsity and sommer is much slower even without a dense covariance (i.e. a kinship), compared to lme4::lmer() or asreml().
To use asreml I require to access the license available only on cbsurobbins.biohpc.cornell.edu.
This is the only step like this in the pipeline.
cbsurobbins is using a SLURM job scheduler now. According to instructions, start an interactive bash shell with requested amount of recources, as follows:
screen;
cd ~/IITA_2021GS/;
salloc -n 8 --mem=60G --time=06:00:00;
# salloc: Granted job allocation 833
# salloc: Waiting for resource configuration
# salloc: Nodes cbsurobbins are ready for job
R;library(tidyverse); library(magrittr);
library(genomicMateSelectR)
dbdata<-readRDS(here::here("output","IITA_ExptDesignsDetected_2021Aug08.rds"))
traits<-c("MCMDS","DM","PLTHT","BRNHT1","BRLVLS","HI",
"logDYLD", # <-- logDYLD now included.
"logFYLD","logTOPYLD","logRTNO","TCHART","LCHROMO","ACHROMO","BCHROMO")
# **Nest by trait.** Need to restructure the data from per-trial by regrouping by trait.
dbdata<-nestDesignsDetectedByTraits(dbdata,traits)dbdata %>% mutate(N_blups=map_dbl(MultiTrialTraitData,nrow)) %>% rmarkdown::paged_table()dbdata %<>%
mutate(fixedFormula=ifelse(Trait %in% c("logDYLD","logFYLD","logRTNO","logTOPYLD"),
"Value ~ yearInLoc + PropNOHAV","Value ~ yearInLoc"),
randFormula=paste0("~idv(GID) + idv(trialInLocYr) + at(CompleteBlocks,'Yes'):repInTrial ",
"+ at(IncompleteBlocks,'Yes'):blockInRep"))
dbdata %>%
mutate(Nobs=map_dbl(MultiTrialTraitData,nrow)) %>%
select(Trait,Nobs,fixedFormula,randFormula) %>%
rmarkdown::paged_table()Includes rounds of outlier removal and re-fitting.
fitASfunc<-function(fixedFormula,randFormula,MultiTrialTraitData,...){
# test arguments for function
# ----------------------
# MultiTrialTraitData<-dbdata$MultiTrialTraitData[[7]]
# #Trait<-dbdata$Trait[[7]]
# fixedFormula<-dbdata$fixedFormula[[7]]
# randFormula<-dbdata$randFormula[[7]]
#test<-fitASfunc(fixedFormula,randFormula,MultiTrialTraitData)
# ----------------------
MultiTrialTraitData %<>%
mutate(across(c(GID,yearInLoc,
CompleteBlocks,
IncompleteBlocks,
trialInLocYr,
repInTrial,
blockInRep),as.factor)) %>%
droplevels
require(asreml);
fixedFormula<-as.formula(fixedFormula)
randFormula<-as.formula(randFormula)
# fit asreml
out<-asreml(fixed = fixedFormula,
random = randFormula,
data = MultiTrialTraitData,
maxiter = 40, workspace=1000e6,
na.method.X="omit")
#### extract residuals - Round 1
outliers1<-which(abs(scale(out$residuals))>3.3)
if(length(outliers1)>0){
x<-MultiTrialTraitData[-outliers1,]
# re-fit
out<-asreml(fixed = fixedFormula,
random = randFormula,
data = x,
maxiter = 40, workspace=1000e6,
na.method.X="omit")
#### extract residuals - Round 2
outliers2<-which(abs(scale(out$residuals))>3.3)
if(length(outliers2)>0){
#### remove outliers
x<-x[-outliers2,]
# final re-fit
out<-asreml(fixed = fixedFormula,
random = randFormula,
data = x, maxiter = 40,workspace=1000e6,
na.method.X="omit")
}
}
if(length(outliers1)==0){ outliers1<-NULL }
if(length(outliers2)==0){ outliers2<-NULL }
ll<-summary(out,all=T)$loglik
varcomp<-summary(out,all=T)$varcomp
Vg<-varcomp["GID!GID.var","component"]
Ve<-varcomp["R!variance","component"]
H2=Vg/(Vg+Ve)
blups<-summary(out,all=T)$coef.random %>%
as.data.frame %>%
rownames_to_column(var = "GID") %>%
dplyr::select(GID,solution,`std error`) %>%
filter(grepl("GID",GID)) %>%
rename(BLUP=solution) %>%
mutate(GID=gsub("GID_","",GID),
PEV=`std error`^2, # asreml specific
REL=1-(PEV/Vg), # Reliability
drgBLUP=BLUP/REL, # deregressed BLUP
WT=(1-H2)/((0.1 + (1-REL)/REL)*H2)) # weight for use in Stage 2
out<-tibble(loglik=ll,Vg,Ve,H2,
blups=list(blups),
varcomp=list(varcomp),
outliers1=list(outliers1),
outliers2=list(outliers2))
gc()
return(out) }Ran in small chunks. Still learning SLURM scheduler used on server.
library(asreml)
require(furrr); plan(multisession, workers = 4)
options(future.globals.maxSize=+Inf); options(future.rng.onMisuse="ignore")
test<-dbdata %>%
slice(1:4) %>%
mutate(fitAS=future_pmap(.,fitASfunc))
saveRDS(test,file=here::here("output","test_2021Aug09.rds"))
plan(sequential)
rm(test); gc()
require(furrr); plan(multisession, workers = 5)
options(future.globals.maxSize=+Inf); options(future.rng.onMisuse="ignore")
test1<-dbdata %>%
slice(5:9) %>%
mutate(fitAS=future_pmap(.,fitASfunc))
plan(sequential)
saveRDS(test1,file=here::here("output","test1_2021Aug09.rds"))
rm(test1); gc();
require(furrr); plan(multisession, workers = 5)
options(future.globals.maxSize=+Inf); options(future.rng.onMisuse="ignore")
test2<-dbdata %>%
slice(10:14) %>%
mutate(fitAS=future_pmap(.,fitASfunc))
plan(sequential)
saveRDS(test2,file=here::here("output","test2_2021Aug09.rds"))
dbdata<-readRDS(here::here("output","test_2021Aug09.rds")) %>%
bind_rows(readRDS(here::here("output","test1_2021Aug09.rds"))) %>%
bind_rows(readRDS(here::here("output","test2_2021Aug09.rds"))) %>%
select(-fixedFormula,-randFormula,-MultiTrialTraitData)
dbdata %<>%
unnest(fitAS)saveRDS(dbdata,file=here::here("output","IITA_blupsForModelTraining_twostage_asreml_2021Aug09.rds"))See Results: Home for plots and summary tables.
Set-up the singularity shell and R environment
# 1) start a screen shell
screen; # or screen -r if re-attaching...
# 2) start the singularity Linux shell inside that
#singularity shell /workdir/$USER/rocker.sif;
singularity pull rocker.sif docker://rocker/tidyverse:latest;
singularity shell ~/rocker.sif;
#singularity shell ~/rocker2.sif;
# Project directory, so R will use as working dir.
cd /home/mw489/IITA_2021GS/;
# 3) Start R
R
# libPath<-"/home/mw489/R/x86_64-pc-linux-gnu-library/4.1"
# withr::with_libpaths(new=libPath, devtools::install_github("wolfemd/genomicMateSelectR", ref = 'master'))# library(tidyverse); library(magrittr);
# library(genomicMateSelectR)
# dbdata<-readRDS(here::here("output","IITA_ExptDesignsDetected_2021Aug08.rds"))
# traits<-c("MCMDS","DM","PLTHT","BRNHT1","BRLVLS","HI",
# "logDYLD", # <-- logDYLD now included.
# "logFYLD","logTOPYLD","logRTNO","TCHART","LCHROMO","ACHROMO","BCHROMO")
#
# # **Nest by trait.** Need to restructure the data from per-trial by regrouping by trait.
# dbdata<-nestDesignsDetectedByTraits(dbdata,traits)# RhpcBLASctl::blas_set_num_threads(56)
#
# MultiTrialTraitData<-dbdata$MultiTrialTraitData[[2]]
# fixedFormula<-"Value ~ yearInLoc"
# randFormula<-paste0("~vs(GID) + vs(trialInLocYr) + vs(at(CompleteBlocks,'Yes'),repInTrial) + vs(at(IncompleteBlocks,'Yes'),blockInRep)")
# library(sommer)
# fit <- sommer::mmer(fixed = as.formula(fixedFormula),
# random = as.formula(randFormula),
# weights = WT,
# data=MultiTrialTraitData,
# date.warning = F,
# getPEV = F)
# MultiTrialTraitData %>% distinct(GID)
# Error: cannot allocate vector of size 537.8 Gb
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] genomicMateSelectR_0.2.0 magrittr_2.0.1 forcats_0.5.1
[4] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[7] readr_2.0.0 tidyr_1.1.3 tibble_3.1.3
[10] ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 lubridate_1.7.10 here_1.0.1 assertthat_0.2.1
[5] rprojroot_2.0.2 digest_0.6.27 utf8_1.2.2 R6_2.5.0
[9] cellranger_1.1.0 backports_1.2.1 reprex_2.0.1 evaluate_0.14
[13] httr_1.4.2 pillar_1.6.2 rlang_0.4.11 readxl_1.3.1
[17] rstudioapi_0.13 whisker_0.4 jquerylib_0.1.4 rmarkdown_2.10
[21] munsell_0.5.0 broom_0.7.9 compiler_4.1.0 httpuv_1.6.1
[25] modelr_0.1.8 xfun_0.25 pkgconfig_2.0.3 htmltools_0.5.1.1
[29] tidyselect_1.1.1 fansi_0.5.0 crayon_1.4.1 tzdb_0.1.2
[33] dbplyr_2.1.1 withr_2.4.2 later_1.2.0 grid_4.1.0
[37] jsonlite_1.7.2 gtable_0.3.0 lifecycle_1.0.0 DBI_1.1.1
[41] git2r_0.28.0 scales_1.1.1 cli_3.0.1 stringi_1.7.3
[45] fs_1.5.0 promises_1.2.0.1 xml2_1.3.2 bslib_0.2.5.1
[49] ellipsis_0.3.2 generics_0.1.0 vctrs_0.3.8 tools_4.1.0
[53] glue_1.4.2 hms_1.1.0 yaml_2.2.1 colorspace_2.0-2
[57] rvest_1.0.1 knitr_1.33 haven_2.4.3 sass_0.4.0