Last updated: 2020-12-23
Checks: 7 0
Knit directory: TARI_2020GS/
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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
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | fae176a | wolfemd | 2020-12-23 | Publish the first set of analyses and files for TARI 2020 GS. |
Two-stage procedure:
Work below represents Stage 1 of the Two-stage procedure.
# activate multithread OpenBLAS for fast matrix algebra
export OMP_NUM_THREADS=56
rm(list = ls())
library(tidyverse)
library(magrittr)
source(here::here("code", "gsFunctions.R"))
<- readRDS(here::here("output", "TARI_ExptDesignsDetected_2020Dec18.rds"))
dbdata <- c("MCMDS", "MCBSDS", "CBSDRS", "CGMS1", "CGMS2", "DM", "PLTHT", "HI", "logDYLD",
traits "logFYLD", "logTOPYLD", "logRTNO")
# **Nest by trait.** Need to restructure the data from per-trial by regrouping by
# trait.
<- nestDesignsDetectedByTraits(dbdata, traits) dbdata
%>% mutate(N_blups = map_dbl(MultiTrialTraitData, nrow)) %>% rmarkdown::paged_table() dbdata
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()
.
%<>% mutate(fixedFormula = ifelse(Trait %in% c("logDYLD", "logFYLD", "logRTNO",
dbdata "logTOPYLD"), "Value ~ yearInLoc + PropNOHAV", "Value ~ yearInLoc"), randFormula = paste0("~idv(GID) + idv(trialInLocYr) + at(CompleteBlocks,'Yes'):repInTrial ",
"+ at(IncompleteBlocks,'Yes'):blockInRep"))
%>% mutate(Nobs = map_dbl(MultiTrialTraitData, nrow)) %>% select(Trait, Nobs,
dbdata %>% rmarkdown::paged_table() fixedFormula, randFormula)
# 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.
<-function(fixedFormula,randFormula,MultiTrialTraitData,...){
fitASfunc# 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);
<-as.formula(fixedFormula)
fixedFormula<-as.formula(randFormula)
randFormula# fit asreml
<-asreml(fixed = fixedFormula,
outrandom = randFormula,
data = MultiTrialTraitData,
maxiter = 40, workspace=800e6, na.method.X = "omit")
#### extract residuals - Round 1
<-which(abs(scale(out$residuals))>3.3)
outliers1
if(length(outliers1)>0){
<-MultiTrialTraitData[-outliers1,]
x# re-fit
<-asreml(fixed = fixedFormula,
outrandom = randFormula,
data = x,
maxiter = 40, workspace=800e6, na.method.X = "omit")
#### extract residuals - Round 2
<-which(abs(scale(out$residuals))>3.3)
outliers2if(length(outliers2)>0){
#### remove outliers
<-x[-outliers2,]
x# final re-fit
<-asreml(fixed = fixedFormula,
outrandom = randFormula,
data = x, maxiter = 40,workspace=800e6, na.method.X = "omit")
}
}if(length(outliers1)==0){ outliers1<-NULL }
if(length(outliers2)==0){ outliers2<-NULL }
<-summary(out,all=T)$loglik
ll<-summary(out,all=T)$varcomp
varcomp<-varcomp["GID!GID.var","component"]
Vg<-varcomp["R!variance","component"]
Ve=Vg/(Vg+Ve)
H2<-summary(out,all=T)$coef.random %>%
blups%>%
as.data.frame rownames_to_column(var = "GID") %>%
::select(GID,solution,`std error`) %>%
dplyrfilter(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
<-tibble(loglik=ll,Vg,Ve,H2,
outblups=list(blups),
varcomp=list(varcomp),
outliers1=list(outliers1),
outliers2=list(outliers2))
return(out) }
library(furrr)
options(mc.cores = 14)
plan(multiprocess)
library(asreml)
%<>% mutate(fitAS = future_pmap(., fitASfunc))
dbdata %<>% select(-fixedFormula, -randFormula, -MultiTrialTraitData) %>% unnest(fitAS) dbdata
saveRDS(dbdata, file = here::here("output", "tari_blupsForModelTraining_twostage_asreml_2020Dec20.rds"))
See Results: Home for plots and summary tables.
sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
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_0.5.0 stringr_1.4.0 dplyr_1.0.2
[5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
[9] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.19 haven_2.3.1 colorspace_2.0-0
[5] vctrs_0.3.5 generics_0.1.0 htmltools_0.5.0 yaml_2.2.1
[9] rlang_0.4.9 later_1.1.0.1 pillar_1.4.7 withr_2.3.0
[13] glue_1.4.2 DBI_1.1.0 dbplyr_2.0.0 modelr_0.1.8
[17] readxl_1.3.1 lifecycle_0.2.0 munsell_0.5.0 gtable_0.3.0
[21] cellranger_1.1.0 rvest_0.3.6 evaluate_0.14 knitr_1.30
[25] ps_1.5.0 httpuv_1.5.4 fansi_0.4.1 broom_0.7.2
[29] Rcpp_1.0.5 promises_1.1.1 backports_1.2.1 scales_1.1.1
[33] formatR_1.7 jsonlite_1.7.2 fs_1.5.0 hms_0.5.3
[37] digest_0.6.27 stringi_1.5.3 rprojroot_2.0.2 grid_4.0.2
[41] here_1.0.1 cli_2.2.0 tools_4.0.2 crayon_1.3.4
[45] whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.1 xml2_1.3.2
[49] reprex_0.3.0 lubridate_1.7.9.2 rstudioapi_0.13 assertthat_0.2.1
[53] rmarkdown_2.6 httr_1.4.2 R6_2.5.0 git2r_0.27.1
[57] compiler_4.0.2