Last updated: 2021-03-24

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Knit directory: PredictOutbredCrossVar/

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    Modified:   analysis/NGCleadersCall.Rmd
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    Modified:   code/getDirectionalDomGenomicBLUPs.R
    Modified:   code/getDirectionalDomMtCrossMeanPreds.R
    Modified:   code/getDirectionalDomMtCrossVarBVpreds.R
    Modified:   code/getDirectionalDomMtCrossVarTGVpreds.R
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    Modified:   data/blups_forawcdata.rds
    Modified:   data/genmap_awc_May2020.rds
    Modified:   data/parentwise_crossVal_folds.rds
    Modified:   data/ped_awc.rds
    Modified:   data/selection_index_weights_4traits.rds
    Modified:   output/CrossesToPredict_top100stdSI_and_209originalParents.rds
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    Modified:   output/crossRealizations/realizedCrossMeans_BLUPs.rds
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    Modified:   output/obsVSpredUC.rds
    Modified:   output/obsVSpredVars.rds
    Modified:   output/pmv_DirectionalDom_varcomps_geneticgroups.rds
    Modified:   output/pmv_varcomps_geneticgroups.rds
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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 4.0.3 (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] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.6        whisker_0.4       knitr_1.31        magrittr_2.0.1   
 [5] R6_2.5.0          rlang_0.4.10      fansi_0.4.2       stringr_1.4.0    
 [9] tools_4.0.3       xfun_0.22         utf8_1.2.1        git2r_0.28.0     
[13] jquerylib_0.1.3   htmltools_0.5.1.1 ellipsis_0.3.1    rprojroot_2.0.2  
[17] yaml_2.2.1        digest_0.6.27     tibble_3.1.0      lifecycle_1.0.0  
[21] crayon_1.4.1      later_1.1.0.1     sass_0.3.1        vctrs_0.3.6      
[25] promises_1.2.0.1  fs_1.5.0          glue_1.4.2        evaluate_0.14    
[29] rmarkdown_2.7     stringi_1.5.3     bslib_0.2.4       compiler_4.0.3   
[33] pillar_1.5.1      jsonlite_1.7.2    httpuv_1.5.5      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