Last updated: 2021-01-03

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Knit directory: PredictOutbredCrossVar/

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    Deleted:    output/crossPredictions/predUntestedCrosses_top100stdSI_chunk2_A_predVarsAndCovars.rds
    Deleted:    output/crossPredictions/predUntestedCrosses_top100stdSI_chunk3_AD_predVarsAndCovars.rds
    Deleted:    output/crossPredictions/predUntestedCrosses_top100stdSI_chunk3_A_predVarsAndCovars.rds
    Deleted:    output/crossPredictions/predUntestedCrosses_top100stdSI_chunk4_AD_predVarsAndCovars.rds
    Deleted:    output/crossPredictions/predUntestedCrosses_top100stdSI_chunk4_A_predVarsAndCovars.rds
    Deleted:    output/crossPredictions/predUntestedCrosses_top100stdSI_chunk5_AD_predVarsAndCovars.rds
    Deleted:    output/crossPredictions/predUntestedCrosses_top100stdSI_chunk5_A_predVarsAndCovars.rds
    Modified:   output/crossPredictions/predictedCrossMeans.rds
    Modified:   output/crossPredictions/predictedCrossMeans_DirectionalDom_tidy_withSelIndices.rds
    Deleted:    output/crossPredictions/predictedCrossMeans_GCA_SCA.rds
    Modified:   output/crossPredictions/predictedCrossMeans_tidy_withSelIndices.rds
    Modified:   output/crossPredictions/predictedCrossVars_DirectionalDom_tidy_withSelIndices.rds
    Deleted:    output/crossPredictions/predictedCrossVars_GCA_SCA.rds
    Deleted:    output/crossPredictions/predictedCrossVars_chunk1.rds
    Deleted:    output/crossPredictions/predictedCrossVars_chunk2.rds
    Deleted:    output/crossPredictions/predictedCrossVars_chunk3.rds
    Deleted:    output/crossPredictions/predictedCrossVars_chunk4.rds
    Deleted:    output/crossPredictions/predictedCrossVars_chunk5.rds
    Modified:   output/crossPredictions/predictedCrossVars_tidy_withSelIndices.rds
    Modified:   output/crossPredictions/predictedDirectionalDomCrossMeans.rds
    Deleted:    output/crossPredictions/predictedDirectionalDomCrossVarBVs_chunk1.rds
    Deleted:    output/crossPredictions/predictedDirectionalDomCrossVarBVs_chunk2.rds
    Deleted:    output/crossPredictions/predictedDirectionalDomCrossVarBVs_chunk3.rds
    Deleted:    output/crossPredictions/predictedDirectionalDomCrossVarBVs_chunk4.rds
    Deleted:    output/crossPredictions/predictedDirectionalDomCrossVarBVs_chunk5.rds
    Deleted:    output/crossPredictions/predictedDirectionalDomCrossVars_chunk1.rds
    Deleted:    output/crossPredictions/predictedDirectionalDomCrossVars_chunk2.rds
    Deleted:    output/crossPredictions/predictedDirectionalDomCrossVars_chunk3.rds
    Deleted:    output/crossPredictions/predictedDirectionalDomCrossVars_chunk4.rds
    Deleted:    output/crossPredictions/predictedDirectionalDomCrossVars_chunk5.rds
    Modified:   output/crossPredictions/predictedUntestedCrossMeansBV.rds
    Modified:   output/crossPredictions/predictedUntestedCrossMeansDirDom.rds
    Modified:   output/crossPredictions/predictedUntestedCrossMeansTGV.rds
    Modified:   output/crossPredictions/predictedUntestedCrossMeans_SelIndices.rds
    Modified:   output/crossPredictions/predictedUntestedCrossMeans_tidy_traits.rds
    Modified:   output/crossPredictions/predictedUntestedCrossVars_SelIndices.rds
    Modified:   output/crossPredictions/predictedUntestedCrossVars_tidy_traits.rds
    Modified:   output/crossRealizations/realizedCrossMeans.rds
    Modified:   output/crossRealizations/realizedCrossMeans_BLUPs.rds
    Modified:   output/crossRealizations/realizedCrossMetrics.rds
    Modified:   output/crossRealizations/realizedCrossVars.rds
    Modified:   output/crossRealizations/realizedCrossVars_BLUPs.rds
    Modified:   output/crossRealizations/realized_cross_means_and_covs_traits.rds
    Modified:   output/crossRealizations/realized_cross_means_and_vars_selindices.rds
    Modified:   output/gblups_DirectionalDom_parentwise_crossVal_folds.rds
    Modified:   output/gblups_geneticgroups.rds
    Modified:   output/gblups_parentwise_crossVal_folds.rds
    Modified:   output/gebvs_ModelA_GroupAll_stdSI.rds
    Modified:   output/mtMarkerEffects/mt_All_A.rds
    Modified:   output/mtMarkerEffects/mt_All_AD.rds
    Modified:   output/mtMarkerEffects/mt_All_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_GG_A.rds
    Modified:   output/mtMarkerEffects/mt_GG_AD.rds
    Modified:   output/mtMarkerEffects/mt_GG_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold1_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold1_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold1_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold1_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold1_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold1_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold2_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold2_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold2_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold2_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold2_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold2_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold3_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold3_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold3_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold3_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold3_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold3_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold4_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold4_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold4_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold4_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold4_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold4_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold5_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold5_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold5_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold5_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold5_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat1_Fold5_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold1_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold1_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold1_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold1_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold1_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold1_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold2_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold2_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold2_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold2_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold2_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold2_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold3_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold3_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold3_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold3_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold3_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold3_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold4_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold4_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold4_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold4_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold4_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold4_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold5_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold5_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold5_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold5_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold5_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat2_Fold5_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold1_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold1_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold1_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold1_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold1_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold1_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold2_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold2_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold2_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold2_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold2_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold2_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold3_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold3_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold3_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold3_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold3_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold3_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold4_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold4_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold4_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold4_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold4_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold4_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold5_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold5_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold5_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold5_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold5_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat3_Fold5_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold1_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold1_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold1_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold1_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold1_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold1_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold2_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold2_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold2_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold2_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold2_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold2_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold3_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold3_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold3_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold3_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold3_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold3_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold4_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold4_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold4_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold4_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold4_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold4_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold5_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold5_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold5_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold5_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold5_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold5_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold1_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold1_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold1_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold1_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold1_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold1_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold2_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold2_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold2_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold2_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold2_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold2_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold3_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold3_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold3_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold3_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold3_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold3_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold4_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold4_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold4_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold4_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold4_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold4_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold5_testset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold5_testset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold5_testset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold5_trainset_A.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold5_trainset_AD.rds
    Modified:   output/mtMarkerEffects/mt_Repeat5_Fold5_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_TMS13_A.rds
    Modified:   output/mtMarkerEffects/mt_TMS13_AD.rds
    Modified:   output/mtMarkerEffects/mt_TMS13_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_TMS14_A.rds
    Modified:   output/mtMarkerEffects/mt_TMS14_AD.rds
    Modified:   output/mtMarkerEffects/mt_TMS14_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_TMS15_A.rds
    Modified:   output/mtMarkerEffects/mt_TMS15_AD.rds
    Modified:   output/mtMarkerEffects/mt_TMS15_DirectionalDom.rds
    Modified:   output/obsVSpredMeans.rds
    Modified:   output/obsVSpredUC.rds
    Modified:   output/obsVSpredVars.rds
    Modified:   output/pmv_DirectionalDom_varcomps_geneticgroups.rds
    Modified:   output/pmv_varcomps_geneticgroups.rds
    Modified:   output/pmv_varcomps_geneticgroups_tidy_includingSIvars.rds
    Modified:   workflowr_log.R

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/predictCrossVars.Rmd) and HTML (docs/predictCrossVars.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
html	22e6c87	wolfemd	2021-01-03	Build site.
html	fb29cd8	wolfemd	2021-01-02	Build site.
Rmd	fd91cd9	wolfemd	2021-01-02	Compile submission version main figures.
Rmd	2228d20	wolfemd	2020-12-15	Work in progress. Finished re-doing predictions with both self-cross handling and PMV VarD bugs fixed. Discovered NEW bug in DirDom results.
Rmd	e736c60	wolfemd	2020-12-02	Re-predict self-crosses in the cross-val scheme using updated/corrected predCrossVar package.
Rmd	2e13628	wolfemd	2020-11-25	Misc minor changes
html	3dbb1e8	wolfemd	2020-10-08	Site built for first COMPLETE draft, shared with co-authors.
html	b06eee7	wolfemd	2020-08-31	Build site.
html	7a4e168	wolfemd	2020-07-31	Build site.
html	a5ac99f	wolfemd	2020-07-31	Build site.
html	ff4fc8b	wolfemd	2020-07-31	Build site.
html	e6f8fe8	wolfemd	2020-07-31	Build site.
Rmd	07509a8	wolfemd	2020-07-31	Organized and ready to be published as a workflowr HTML page.
Rmd	a1950f7	wolfemd	2020-07-29	Directional dominance fully implemented, except in prediction of untested crosses. Presentation to NGC Leader’s call included.
Rmd	c96001f	wolfemd	2020-06-28	Results and organization nearly complete. Prediction of Usefulness might be wrong (NEEDS REVIEW).
Rmd	c0b292a	wolfemd	2020-06-23	Most analyses coded and complete. Moved all wrapper functions to code/*.R scripts.
Rmd	4846c0e	wolfemd	2020-06-14	All analyses completed for one-rep of parent-wise cross-val. as of June 9 Edinburgh CBDG talk.

Predict cross (co)variances

Install package .

# devtools::install_github("wolfemd/predCrossVar", ref = 'master', force=T)

Models A and AD

Set-up

# activate multithread OpenBLAS
export OMP_NUM_THREADS=88

rm(list=ls()); gc()
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);

# BLUPs -----------
blups<-readRDS(here::here("data","blups_forawcdata.rds")) %>% 
  select(Trait,blups) %>% 
  unnest(blups) %>% 
  select(Trait,germplasmName,drgBLUP) %>% 
  spread(Trait,drgBLUP) %>%  
  select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order

# Training datasets -----------
parentfolds<-readRDS(file = here::here("data","parentwise_crossVal_folds.rds")) %>% 
  rename(Repeat=id,Fold=id2) %>% 
  select(Repeat,Fold,testparents,trainset) %>% 
  pivot_longer(c(trainset), # exclude the testsets
               names_to = "Dataset",
               values_to = "sampleIDs") %>% 
  crossing(Model=c("A","AD")) %>% 
  arrange(desc(Dataset),Repeat,Fold) %>% 
  mutate(blups=map(sampleIDs,~filter(blups,germplasmName %in% .)),
         outName=paste0("mt_",Repeat,"_",Fold,"_",Dataset,"_",Model))

# Pedigree -----------
ped<-readRDS(here::here("data","ped_awc.rds")) %>%
  distinct(sireID,damID)

# Crosses To Predict -------------
parentfolds %<>% 
  mutate(CrossesToPredict=map(testparents,~filter(ped,sireID %in% . | damID %in% .)))

# Recomb frequency matrix ------------
recombFreqMat<-readRDS(here::here("data","recombFreqMat_1minus2c_awcmap_May2020.rds"))

# Haplotype Matrix ------------
haploMat<-readRDS(file=here::here("data","haps_awc.rds"))
parenthaps<-sort(c(paste0(union(ped$sireID,ped$damID),"_HapA"),
                   paste0(union(ped$sireID,ped$damID),"_HapB")))
haploMat<-haploMat[parenthaps,colnames(recombFreqMat)]; rm(parenthaps); dim(haploMat)

# for consistency
parentfolds %<>% 
  rename(outprefix=outName)

# Parallelization specs ---------
require(furrr); options(future.globals.maxSize=50000*1024^2)
ncores<-10; 

# MCMC params ------
nIter<-30000; burnIn<-5000; thin<-5

# Path for output ----------
outpath<-"output/crossPredictions"

# getUntestedMtCrossVarPreds function -------------
## Function to run for each rep-fold-Model (==unique set of marker effects), predict the relevant cross variances.
source(here::here("code","getMtCrossVarPreds.R"))

nchunks<-5
parentfolds %<>% 
  mutate(Chunk=rep(1:nchunks, each=ceiling(nrow(.)/nchunks), length.out=nrow(.))) %>% 
  nest(data=c(-Chunk))

Run variance predictions

# cbsulm17 - done
chunk<-1;
# cbsulm27 - done
chunk<-2;
# cbsulm22 - done
chunk<-3;
# cbsulm09 - (cbsulm15 - done)
chunk<-4;
# cbsulm10 (cbsulm17 - done)
chunk<-5;

# Start run on each server / chunk: Done
predictedCrossVars<-parentfolds %>% 
  slice(chunk) %>% 
  unnest(data) %>% 
  mutate(crossVars=pmap(.,getMtCrossVarPreds,
                        outpath="output/crossPredictions",
                        predType="PMV",nIter=nIter,burnIn=burnIn,thin=thin,
                        recombFreqMat=recombFreqMat,haploMat=haploMat,ncores=ncores))
saveRDS(predictedCrossVars,
        file=here::here("output/crossPredictions",paste0("predictedCrossVars_chunk",chunk,"_2Dec2020.rds")))

Model DirDomAD

Set-up

# activate multithread OpenBLAS
export OMP_NUM_THREADS=112

rm(list=ls()); gc()
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);

# BLUPs -----------
blups<-readRDS(here::here("data","blups_forawcdata.rds")) %>% 
  select(Trait,blups) %>% 
  unnest(blups) %>% 
  select(Trait,germplasmName,drgBLUP) %>% 
  spread(Trait,drgBLUP) %>%  
  select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order

# Training datasets -----------
parentfolds<-readRDS(file = here::here("data","parentwise_crossVal_folds.rds")) %>% 
  rename(Repeat=id,Fold=id2) %>% 
  select(Repeat,Fold,testparents,trainset) %>% 
  pivot_longer(c(trainset), # exclude the testsets
               names_to = "Dataset",
               values_to = "sampleIDs") %>% 
  mutate(Model="DirectionalDom") %>% 
  arrange(desc(Dataset),Repeat,Fold) %>% 
  mutate(blups=map(sampleIDs,~filter(blups,germplasmName %in% .)),
         outName=paste0("mt_",Repeat,"_",Fold,"_",Dataset,"_",Model))

# Pedigree -----------
ped<-readRDS(here::here("data","ped_awc.rds")) %>%
  distinct(sireID,damID)

# Crosses To Predict -------------
parentfolds %<>% 
  mutate(CrossesToPredict=map(testparents,~filter(ped,sireID %in% . | damID %in% .)))

# Recomb frequency matrix ------------
recombFreqMat<-readRDS(here::here("data","recombFreqMat_1minus2c_awcmap_May2020.rds"))

# Haplotype Matrix ------------
haploMat<-readRDS(file=here::here("data","haps_awc.rds"))
parenthaps<-sort(c(paste0(union(ped$sireID,ped$damID),"_HapA"),
                   paste0(union(ped$sireID,ped$damID),"_HapB")))
haploMat<-haploMat[parenthaps,colnames(recombFreqMat)]; rm(parenthaps); dim(haploMat)

# for consistency
parentfolds %<>% 
  rename(outprefix=outName)

# Parallelization specs ---------
require(furrr); options(future.globals.maxSize=50000*1024^2)
ncores<-10; 

# MCMC params ------
nIter<-30000; burnIn<-5000; thin<-5

# Path for output ----------
outpath<-"output/crossPredictions"

# Divide parentfolds into chunks for each server ------------
nchunks<-4
parentfolds %<>% 
  mutate(Chunk=rep(1:nchunks, each=ceiling(nrow(.)/nchunks), length.out=nrow(.))) %>% 
  nest(data=c(-Chunk))

Run Var(TGV) predictions

# Wrapper function for runMtCrossVarPredsAD.
# For each rep-fold (==unique set of marker effects), predict the relevant cross variances.
# This version is for a directional dominance model.
# The only difference from getMtCrossVarPreds is that the inbreeding effect
# For each trait is extract from the BGLR output,
# divided by N snps and added to the vector of SNP effects
# The output predicted variances should be suitable to
# compute predVar(TGV) = predVar(A) + predVar(D)
source(here::here("code","getDirectionalDomMtCrossVarTGVpreds.R"))

# cbsulm13 - Done!
chunk<-1;
# cbsulm15 - Done!
chunk<-2;
# cbsulm17 - Done!
chunk<-3;
# cbsulm26 - Done!
chunk<-4;

# Start run on each server / chunk: Done
predictedCrossVars<-parentfolds %>% 
  slice(chunk) %>% 
  unnest(data) %>% 
  mutate(crossVars=pmap(.,getDirectionalDomMtCrossVarTGVpreds,
                        outpath="output/crossPredictions",
                        predType="PMV",nIter=nIter,burnIn=burnIn,thin=thin,
                        recombFreqMat=recombFreqMat,haploMat=haploMat,ncores=ncores))
saveRDS(predictedCrossVars,
        file=here::here("output/crossPredictions",
                        paste0("predictedDirectionalDomCrossVarTGVs_chunk",chunk,"_15Dec2020.rds")))

Run Var(BV) predictions

# Wrapper function for runMtCrossVarPredsA.
# For each rep-fold (==unique set of marker effects), predict the relevant cross variances.
# This version is for a directional dominance model.
# The only difference from getMtCrossVarPreds are:
# 1. that the inbreeding effect for each trait is extract from the BGLR output,
# divided by N snps and added to the vector of SNP effects
# 2. the allele substitution effects are computed as: a+d(q-p)
# runMtCrossVarPredsA() is run and the 
# output predicted variances should be the predVar(BV) for each family 
source(here::here("code","getDirectionalDomMtCrossVarBVpreds.R"))

# SNP data ------------
snps<-readRDS(here::here("data","dosages_awc.rds")) %>% 
  remove_invariant(.);

# cbsulm12 - Done!
chunk<-1;
# cbsulm16 - Done! 
chunk<-2;
# cbsulm26 - Done!
chunk<-3;
# cbsulm15 - Done!
chunk<-4;

# Start run on each server / chunk
predictedCrossVars<-parentfolds %>% 
  slice(chunk) %>% 
  unnest(data) %>% 
  mutate(crossVars=pmap(.,getDirectionalDomMtCrossVarBVpreds,
              outpath="output/crossPredictions",
              predType="PMV",nIter=nIter,burnIn=burnIn,thin=thin,
              recombFreqMat=recombFreqMat,haploMat=haploMat,doseMat=snps,ncores=ncores))
saveRDS(predictedCrossVars,
        file=here::here("output/crossPredictions",
                        paste0("predictedDirectionalDomCrossVarBVs_chunk",chunk,"_15Dec2020.rds")))

Predict cross means

Models A and AD

Set-up

# activate multithread OpenBLAS
export OMP_NUM_THREADS=88

rm(list=ls()); gc()
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);

# BLUPs -----------
blups<-readRDS(here::here("data","blups_forawcdata.rds")) %>% 
  select(Trait,blups) %>% 
  unnest(blups) %>% 
  select(Trait,germplasmName,drgBLUP) %>% 
  spread(Trait,drgBLUP) %>%  
  select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order

# Training datasets -----------
parentfolds<-readRDS(file = here::here("data","parentwise_crossVal_folds.rds")) %>% 
  rename(Repeat=id,Fold=id2) %>% 
  select(Repeat,Fold,testparents,trainset) %>% 
  pivot_longer(c(trainset), # exclude the testsets
               names_to = "Dataset",
               values_to = "sampleIDs") %>% 
  crossing(Model=c("A","AD")) %>% 
  arrange(desc(Dataset),Repeat,Fold) %>% 
  mutate(blups=map(sampleIDs,~filter(blups,germplasmName %in% .)),
         outName=paste0("mt_",Repeat,"_",Fold,"_",Dataset,"_",Model))

# Pedigree -----------
ped<-readRDS(here::here("data","ped_awc.rds")) %>%
  distinct(sireID,damID)

# Crosses To Predict -------------
parentfolds %<>% 
  mutate(CrossesToPredict=map(testparents,~filter(ped,sireID %in% . | damID %in% .)))

# SNP data ------------
snps<-readRDS(here::here("data","dosages_awc.rds")) %>% 
  remove_invariant(.); 

# Path for output ----------
outpath<-"output/mtMarkerEffects"

# for consistency
parentfolds %<>% 
  rename(outprefix=outName) 

# Parallelization specs ---------
require(furrr); options(mc.cores=25); plan(multiprocess)
options(future.globals.maxSize=5000*1024^2)

# MCMC params ------
nIter<-30000; burnIn<-5000; thin<-5

# getMtCrossMeanPreds function -------------
## Function to run for each rep-fold-Model (==unique set of marker effects), predict the relevant cross means
source(here::here("code","getMtCrossMeanPreds.R"))

Run mean predictions

# cbsurobbins - Jul 08, 7pm - trivial compute time - COMPLETE
predictedCrossMeans<-parentfolds %>% 
  mutate(crossMeans=future_pmap(.,getMtCrossMeanPreds,doseMat=snps))
saveRDS(predictedCrossMeans,file=here::here("output/crossPredictions","predictedCrossMeans.rds"))

Model DirDomAD

Set-up

# activate multithread OpenBLAS
export OMP_NUM_THREADS=88

rm(list=ls()); gc()
library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);

# BLUPs -----------
blups<-readRDS(here::here("data","blups_forawcdata.rds")) %>% 
  select(Trait,blups) %>% 
  unnest(blups) %>% 
  select(Trait,germplasmName,drgBLUP) %>% 
  spread(Trait,drgBLUP) %>%  
  select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order

# Training datasets -----------
parentfolds<-readRDS(file = here::here("data","parentwise_crossVal_folds.rds")) %>% 
  rename(Repeat=id,Fold=id2) %>% 
  select(Repeat,Fold,testparents,trainset) %>% 
  pivot_longer(c(trainset), # exclude the testsets
               names_to = "Dataset",
               values_to = "sampleIDs") %>% 
  mutate(Model="DirectionalDom") %>% 
  arrange(desc(Dataset),Repeat,Fold) %>% 
  mutate(blups=map(sampleIDs,~filter(blups,germplasmName %in% .)),
         outName=paste0("mt_",Repeat,"_",Fold,"_",Dataset,"_",Model))

# Pedigree -----------
ped<-readRDS(here::here("data","ped_awc.rds")) %>%
  distinct(sireID,damID)

# Crosses To Predict -------------
parentfolds %<>% 
  mutate(CrossesToPredict=map(testparents,~filter(ped,sireID %in% . | damID %in% .)))

# SNP data ------------
snps<-readRDS(here::here("data","dosages_awc.rds")) %>% 
  remove_invariant(.); 

# Path for output ----------
outpath<-"output/crossPredictions"

# for consistency
parentfolds %<>% 
  rename(outprefix=outName) 

# Parallelization specs ---------
require(furrr); options(mc.cores=25); plan(multiprocess)
options(future.globals.maxSize=5000*1024^2)

# MCMC params ------
nIter<-30000; burnIn<-5000; thin<-5

# getDirectionalDomMtCrossMeanPreds function
# Wrapper function: for each rep-fold-Model (==unique set of marker effects), predict the relevant cross means.
# This version is for a directional dominance model.
# Differences from getMtCrossMeanPreds:
# 1. inbreeding effect for each trait is extracted from the BGLR output,
### divided by N snps and added to the vector of SNP effects
# 2. Predicted cross mean GEBV: 
### Compute allele sub effects as a+d(q-p) and multiply by allelic dosages of parents. 
### Cross mean GEBV = 0.5*(GEBV_P1 + GEBV+P2)
# 3. Predicted cross mean GETGV: G = sum( 𝑎(𝑝 − 𝑞 − 𝑦) + 𝑑[2𝑝𝑞 + 𝑦(𝑝 − 𝑞)] )
### a and d being the additive and dominance effects
### p and q being the allele frequencies of one parent 
### y is the difference of freq. between the two parents
source(here::here("code","getDirectionalDomMtCrossMeanPreds.R"))

Run mean predictions

# 
predictedCrossMeans<-parentfolds %>% 
  mutate(crossMeans=future_pmap(.,getDirectionalDomMtCrossMeanPreds,doseMat=snps))
saveRDS(predictedCrossMeans,file=here::here("output/crossPredictions","predictedDirectionalDomCrossMeans.rds"))

Process prediction results

Models A and AD

Tidy predicted variances

library(tidyverse); library(magrittr); library(predCrossVar)
# Predicted (co)variances
predictedCrossVars<-list.files(here::here("output/crossPredictions")) %>% 
  grep("predictedCrossVars_chunk",.,value = T) %>% 
  map_df(.,~readRDS(here::here("output/crossPredictions",.))) %>% 
  select(Repeat,Fold,Model,crossVars) %>% 
  mutate(crossVars=map(crossVars,
                       function(crossVars){
                         out<-crossVars$predictedCrossVars$varcovars %>% 
                           mutate(varcomps=map(varcomps,~.$predictedfamvars)) %>% 
                           unnest(varcomps) %>% 
                           unnest(predVars)
                         return(out)})) %>% 
  unnest(crossVars)

Tidy predicted means

# Predicted means
predmeans<-readRDS(here::here("output/crossPredictions","predictedCrossMeans.rds")) %>% 
  select(Repeat,Fold,Model,crossMeans) %>% 
  unnest_wider(crossMeans) %>% 
  select(-runtime) %>% 
  unnest(predictedCrossMeans) %>% 
  select(-sireGEBV,-damGEBV) %>% 
  pivot_longer(cols = contains("predMean"), values_to = "predMean", names_to = "predOf", names_prefix = "pred", values_drop_na=TRUE)

Compute predictions on SI

# Selection weights
indices<-readRDS(file=here::here("data","selection_index_weights_4traits.rds"))
## Predicted Index Variances
predictedCrossVars_SI<-predictedCrossVars %>% 
  pivot_longer(cols=c(VPM,PMV),names_to = "VarMethod",values_to = "Var") %>% 
  select(Repeat,Fold,Model,sireID,damID,VarComp,VarMethod,Trait1,Trait2,Var) %>% 
  nest(varcovars=c(Trait1,Trait2,Var)) %>% 
  mutate(varcovars=map(varcovars,
                       function(varcovars){
                         # pairwise to square symmetric matrix
                         gmat<-varcovars %>% 
                           spread(Trait2,Var) %>% 
                           column_to_rownames(var = "Trait1") %>% 
                           as.matrix %>% 
                           .[indices$Trait,indices$Trait]
                         gmat[lower.tri(gmat)]<-t(gmat)[lower.tri(gmat)]
                         return(gmat) }))
predictedCrossVars_SI %<>% 
  mutate(stdSI=map_dbl(varcovars,~t(indices$stdSI)%*%.%*%indices$stdSI),
         biofortSI=map_dbl(varcovars,~t(indices$biofortSI)%*%.%*%indices$biofortSI)) %>% 
  select(-varcovars) %>% 
  pivot_longer(cols = c(stdSI,biofortSI),
               names_to = "Trait1", 
               values_to = "Var") %>% 
  mutate(Trait2=Trait1) %>% 
  pivot_wider(names_from = "VarMethod", values_from = "Var")
predictedCrossVars %<>% bind_rows(predictedCrossVars_SI,.)
rm(predictedCrossVars_SI)

## Predicted Index Means
predmeans_SI<-predmeans %>% 
  spread(Trait,predMean) %>% 
  nest(predMeans=all_of(indices$Trait)) %>% 
  mutate(stdSI=map_dbl(predMeans,~as.matrix(.)%*%indices$stdSI),
         biofortSI=map_dbl(predMeans,~as.matrix(.)%*%indices$biofortSI)) %>% 
  select(-predMeans) %>% 
  pivot_longer(cols = c(stdSI,biofortSI), names_to = "Trait", values_to = "predMean")
predmeans %<>% bind_rows(predmeans_SI,.)
rm(predmeans_SI,indices)

–> Save

Save the predicted means and variances in the current form. Output contains Nsegsnps and compute times still.

saveRDS(predmeans,here::here("output/crossPredictions","predictedCrossMeans_tidy_withSelIndices.rds"))
saveRDS(predictedCrossVars,here::here("output/crossPredictions","predictedCrossVars_tidy_withSelIndices.rds"))

Model DirDomAD

Tidy predicted variances

library(tidyverse); library(magrittr); library(predCrossVar)
# Predicted (co)variances
predvars<-bind_rows(list.files(here::here("output/crossPredictions")) %>% 
                      grep("predictedDirectionalDomCrossVarBVs_chunk",.,value = T) %>% 
                      grep("_15Dec2020.rds",.,value = T) %>% 
                      map_df(.,~readRDS(here::here("output/crossPredictions",.))) %>% 
                      select(Repeat,Fold,crossVars) %>% 
                      mutate(Model="DirDomBV"),
                    list.files(here::here("output/crossPredictions")) %>% 
                      grep("predictedDirectionalDomCrossVarTGVs_chunk",.,value = T) %>% 
                      grep("_15Dec2020.rds",.,value = T) %>% 
                      map_df(.,~readRDS(here::here("output/crossPredictions",.))) %>% 
                      select(Repeat,Fold,crossVars) %>% 
                      mutate(Model="DirDomAD")) %>% 
  mutate(crossVars=map(crossVars,
                       function(crossVars){
                         out<-crossVars$predictedCrossVars$varcovars %>% 
                           mutate(varcomps=map(varcomps,~.$predictedfamvars)) %>% 
                           unnest(varcomps) %>% 
                           unnest(predVars)
                         return(out)})) %>% 
  unnest(crossVars)
#predvars %>% count(Model,VarComp)

Tidy predicted means

# Predicted means
predmeans<-readRDS(here::here("output/crossPredictions","predictedDirectionalDomCrossMeans.rds")) %>% 
  select(Repeat,Fold,crossMeans) %>% 
  unnest_wider(crossMeans) %>% 
  select(-runtime) %>% 
  unnest(predictedCrossMeans) %>% 
  select(-sireGEBV,-damGEBV) %>% 
  pivot_longer(cols = contains("predMean"), values_to = "predMean", names_to = "predOf", names_prefix = "pred") %>% 
  select(Repeat,Fold,sireID,damID,Trait,predOf,predMean)

Compute predictions on SI

# Selection weights
indices<-readRDS(file=here::here("data","selection_index_weights_4traits.rds"))
## Predicted Index Variances
predvars_SI<-predvars %>% 
  pivot_longer(cols=c(VPM,PMV),names_to = "VarMethod",values_to = "Var") %>% 
  select(Repeat,Fold,sireID,damID,Trait1,Trait2,Model,VarMethod,VarComp,Var) %>% 
  nest(varcovars=c(Trait1,Trait2,Var)) %>% 
  mutate(varcovars=map(varcovars,
                       function(varcovars){
                         # pairwise to square symmetric matrix
                         gmat<-varcovars %>% 
                           spread(Trait2,Var) %>% 
                           column_to_rownames(var = "Trait1") %>% 
                           as.matrix %>% 
                           .[indices$Trait,indices$Trait]
                         gmat[lower.tri(gmat)]<-t(gmat)[lower.tri(gmat)]
                         return(gmat) }))
predvars_SI %<>% 
  mutate(stdSI=map_dbl(varcovars,~t(indices$stdSI)%*%.%*%indices$stdSI),
         biofortSI=map_dbl(varcovars,~t(indices$biofortSI)%*%.%*%indices$biofortSI)) %>% 
  select(-varcovars) %>% 
  pivot_longer(cols = c(stdSI,biofortSI),
               names_to = "Trait1", 
               values_to = "Var") %>% 
  mutate(Trait2=Trait1) %>% 
  pivot_wider(names_from = "VarMethod", values_from = "Var")
predvars %<>% bind_rows(predvars_SI,.)
rm(predvars_SI)

## Predicted Index Means
predmeans_SI<-predmeans %>% 
  spread(Trait,predMean) %>% 
  nest(predMeans=all_of(indices$Trait)) %>% 
  mutate(stdSI=map_dbl(predMeans,~as.matrix(.)%*%indices$stdSI),
         biofortSI=map_dbl(predMeans,~as.matrix(.)%*%indices$biofortSI)) %>% 
  select(-predMeans) %>% 
  pivot_longer(cols = c(stdSI,biofortSI), names_to = "Trait", values_to = "predMean")
predmeans %<>% bind_rows(predmeans_SI,.)
rm(predmeans_SI)

–> Save

Save the predicted means and variances in the current form. Output contains Nsegsnps and compute times still.

saveRDS(predmeans,here::here("output/crossPredictions","predictedCrossMeans_DirectionalDom_tidy_withSelIndices.rds"))
saveRDS(predvars,here::here("output/crossPredictions","predictedCrossVars_DirectionalDom_tidy_withSelIndices.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] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5      rstudioapi_0.13 whisker_0.4     knitr_1.30     
 [5] magrittr_2.0.1  R6_2.5.0        rlang_0.4.9     stringr_1.4.0  
 [9] tools_4.0.2     xfun_0.19       git2r_0.27.1    htmltools_0.5.0
[13] ellipsis_0.3.1  rprojroot_2.0.2 yaml_2.2.1      digest_0.6.27  
[17] tibble_3.0.4    lifecycle_0.2.0 crayon_1.3.4    later_1.1.0.1  
[21] vctrs_0.3.5     promises_1.1.1  fs_1.5.0        glue_1.4.2     
[25] evaluate_0.14   rmarkdown_2.6   stringi_1.5.3   compiler_4.0.2 
[29] pillar_1.4.7    httpuv_1.5.4    pkgconfig_2.0.3

Predict cross means, variances and co-variances

2020-June-16

Predict cross (co)variances

Models A and AD

Set-up

Run variance predictions

Model DirDomAD

Set-up

Run Var(TGV) predictions

Run Var(BV) predictions

Predict cross means

Models A and AD

Set-up

Run mean predictions

Model DirDomAD

Set-up

Run mean predictions

Process prediction results

Models A and AD

Tidy predicted variances

Tidy predicted means

Compute predictions on SI

–> Save

Model DirDomAD

Tidy predicted variances

Tidy predicted means

Compute predictions on SI

–> Save