Last updated: 2020-12-03
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rm(list = ls())
library(tidyverse)
library(magrittr)
dbdata <- readRDS(here::here("output", "IITA_CleanedTrialData_2020Dec03.rds"))
source(here::here("code", "gsFunctions.R"))
dbdata <- nestByTrials(dbdata)The next step is to check the experimental design of each trial. If you are absolutely certain of the usage of the design variables in your dataset, you might not need this step.
Examples of reasons to do the step below:
One reason it might be important to get this right is that the variance among complete blocks might not be the same among incomplete blocks. If we treat a mixture of complete and incomplete blocks as part of the same random-effect (replicated-within-trial), we assume they have the same variance.
Also error variances might be heterogeneous among different trial-types (blocking scheme available) and/or plot sizes (maxNOHAV).
Detect designs
dbdata <- detectExptDesigns(dbdata)dbdata %>% count(programName, CompleteBlocks, IncompleteBlocks) %>% rmarkdown::paged_table()saveRDS(dbdata, file = here::here("output", "IITA_ExptDesignsDetected_2020Dec03.rds"))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","IITA_ExptDesignsDetected_2020Dec03.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 %<>% dplyr::select(-MaxNOHAV) %>% unnest(TrialData) %>% dplyr::select(programName,
locationName, studyYear, TrialType, studyName, CompleteBlocks, IncompleteBlocks,
yearInLoc, trialInLocYr, repInTrial, blockInRep, observationUnitDbId, germplasmName,
FullSampleName, GID, all_of(traits), PropNOHAV) %>% mutate(IncompleteBlocks = ifelse(IncompleteBlocks ==
TRUE, "Yes", "No"), CompleteBlocks = ifelse(CompleteBlocks == TRUE, "Yes", "No")) %>%
pivot_longer(cols = all_of(traits), names_to = "Trait", values_to = "Value") %>%
filter(!is.na(Value), !is.na(GID)) %>% nest(MultiTrialTraitData = c(-Trait))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("logFYLD", "logRTNO", "logTOPYLD"),
"Value ~ yearInLoc", "Value ~ yearInLoc + PropNOHAV"), 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", "iita_blupsForModelTraining_twostage_asreml_2020Dec03.rds"))
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_1.4.0 httpuv_1.5.4 fansi_0.4.1 broom_0.7.2
[29] Rcpp_1.0.5 promises_1.1.1 backports_1.2.0 scales_1.1.1
[33] formatR_1.7 jsonlite_1.7.1 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_1.0.0 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_2.5 httr_1.4.2 R6_2.5.0 git2r_0.27.1
[57] compiler_4.0.2