Last updated: 2021-05-05
Checks: 7 0
Knit directory: NRCRI_2021GS/
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Untracked: output/genomicPredictions_ModelADE_twostage_NRCRI_2021May03.rds
Untracked: output/genomicPredictions_ModelA_twostage_NRCRI_2021May03.rds
Untracked: output/genomicPredictions_NRCRI_2021May03.csv
Untracked: output/maxNOHAV_byStudy.csv
Unstaged changes:
Modified: README.md
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 | 94ec811 | wolfemd | 2021-05-05 | Completed prediction. Build site and push to GitHub / Cassavabase FTP server. Share to NRCRI. |
Two-stage procedure:
Work below represents Stage 1 of the Two-stage procedure.
# activate multithread OpenBLAS for fast matrix algebra
export OMP_NUM_THREADS=56rm(list=ls())
library(tidyverse); library(magrittr);
source(here::here("code","gsFunctions.R"))
dbdata<-readRDS(here::here("output","NRCRI_ExptDesignsDetected_2021May03.rds"))
traits<-c("CGM","CGMS1","CGMS2","MCMDS",
"DM","DMsg","PLTHT","BRNHT1","HI",
"logDYLD","logFYLD","logTOPYLD","logRTNO")
# **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()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 and sommer is much slower even without a dense covariance (i.e. a kinship), compared to lme4::lmer() or asreml().
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()# randFormula<-paste0("~vs(GID) + vs(trialInLocYr) + vs(at(CompleteBlocks,'Yes'),repInTrial) + vs(at(IncompleteBlocks,'Yes'),blockInRep)")
# library(sommer)
# fit <- mmer(fixed = Value ~ 1 + yearInLoc,
# random = as.formula(randFormula),
# data=trainingdata,
# getPEV=TRUE)Includes rounds of outlier removal and re-fitting.
fitASfunc<-function(fixedFormula,randFormula,MultiTrialTraitData,...){
# test arguments for function
# ----------------------
# MultiTrialTraitData<-dbdata$MultiTrialTraitData[[7]]
# #Trait<-dbdata$Trait[[3]]
# fixedFormula<-dbdata$fixedFormula[[7]]
# randFormula<-dbdata$randFormula[[7]]
# test<-fitASfunc(fixedFormula,randFormula,MultiTrialTraitData)
# ----------------------
require(asreml);
fixedFormula<-as.formula(fixedFormula)
randFormula<-as.formula(randFormula)
# fit asreml
out<-asreml(fixed = fixedFormula,
random = randFormula,
data = MultiTrialTraitData,
maxiter = 40, workspace=800e6, na.method.X = "omit")
#### extract residuals - Round 1
outliers1<-which(abs(scale(out$residuals))>3.3)
if(length(outliers1)>0){
x<-MultiTrialTraitData[-outliers1,]
# re-fit
out<-asreml(fixed = fixedFormula,
random = randFormula,
data = x,
maxiter = 40, workspace=800e6, na.method.X = "omit")
#### extract residuals - Round 2
outliers2<-which(abs(scale(out$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=800e6, 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))
return(out) }library(furrr); options(mc.cores=14); plan(multiprocess)
library(asreml)
dbdata %<>%
mutate(fitAS=future_pmap(.,fitASfunc))
dbdata %<>%
select(-fixedFormula,-randFormula,-MultiTrialTraitData) %>%
unnest(fitAS)saveRDS(dbdata,file=here::here("output","NRCRI_blupsForModelTraining_twostage_asreml_2021May03.rds"))See Results: Home for plots and summary tables.
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/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] magrittr_2.0.1 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.5
[5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.1
[9] ggplot2_3.3.3 tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.22 bslib_0.2.4 haven_2.4.0
[5] colorspace_2.0-0 vctrs_0.3.7 generics_0.1.0 htmltools_0.5.1.1
[9] yaml_2.2.1 utf8_1.2.1 rlang_0.4.10 jquerylib_0.1.3
[13] later_1.1.0.1 pillar_1.6.0 withr_2.4.2 glue_1.4.2
[17] DBI_1.1.1 dbplyr_2.1.1 readxl_1.3.1 modelr_0.1.8
[21] lifecycle_1.0.0 cellranger_1.1.0 munsell_0.5.0 gtable_0.3.0
[25] rvest_1.0.0 evaluate_0.14 knitr_1.32 httpuv_1.5.5
[29] fansi_0.4.2 broom_0.7.6 Rcpp_1.0.6 promises_1.2.0.1
[33] backports_1.2.1 scales_1.1.1 jsonlite_1.7.2 fs_1.5.0
[37] hms_1.0.0 digest_0.6.27 stringi_1.5.3 rprojroot_2.0.2
[41] grid_4.0.3 here_1.0.1 cli_2.4.0 tools_4.0.3
[45] sass_0.3.1 crayon_1.4.1 whisker_0.4 pkgconfig_2.0.3
[49] ellipsis_0.3.1 xml2_1.3.2 reprex_2.0.0 lubridate_1.7.10
[53] rstudioapi_0.13 assertthat_0.2.1 rmarkdown_2.7 httr_1.4.2
[57] R6_2.5.0 git2r_0.28.0 compiler_4.0.3