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/predictUntestedCrosses.Rmd) and HTML (docs/predictUntestedCrosses.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.
Rmd	ef3f7b3	wolfemd	2021-01-02	Compile submission version of all Rmds with outstanding, uncommitted
Rmd	c6f5e72	wolfemd	2020-12-02	Add calc of SI dom. var. (previously computed on tot. variance). Tot var not computed until sup. tables now.
Rmd	fbc72e2	wolfemd	2020-12-02	Re-predicted self-crosses with corrected/updaetd predCrossVar package.
html	34c84f3	wolfemd	2020-10-27	Build site.
Rmd	b4edd2c	wolfemd	2020-10-27	Start workflowr project.
Rmd	7cb77ef	wolfemd	2020-10-27	Revised and improved the “exploration of untested crosses”. Includes a network analysis of parents and matings selected. Returns the selfs to all analyses and actually highlights them now.
html	3dbb1e8	wolfemd	2020-10-08	Site built for first COMPLETE draft, shared with co-authors.
html	2e6904e	wolfemd	2020-10-08	Build site.
Rmd	17b24e6	wolfemd	2020-10-08	First COMPLETE draft. Publish final changes before sharing with
html	b06eee7	wolfemd	2020-08-31	Build site.
Rmd	576392e	wolfemd	2020-08-27	Commiting all code, output and Rmd as of complete draft of results summaries + figures. Next step is to “assemble” the MS from that.
Rmd	fb78731	wolfemd	2020-08-03	Chose to switch to predType=“VPM” for this analysis to save server time.
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.

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

Crosses to predict

The 462 crosses actually made
The 100 clones with top rank on the StdSI
The 209 clones already used as parents

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

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

# Selection weights -----------
indices<-readRDS(file=here::here("data","selection_index_weights_4traits.rds"))

# GEBVs --------------
gebvs<-readRDS(here::here("output","gblups_geneticgroups.rds")) %>% 
  filter(Group=="All",Model=="A") %>% 
  unnest(GBLUPs) %>% 
  select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART")))
gebvMat<-gebvs %>% 
  column_to_rownames(var = "germplasmName") %>% 
  as.matrix
## GEBVs on the "standard" selection index
gebvs %<>% 
  mutate(stdSI=as.numeric(gebvMat%*%indices$stdSI))
saveRDS(gebvs,here::here("output","gebvs_ModelA_GroupAll_stdSI.rds"))
## The top 100 on the index
top100stdSI<-gebvs %>% 
  arrange(desc(stdSI)) %>% 
  slice(1:100) %$% germplasmName
saveRDS(top100stdSI,here::here("output","top100stdSI.rds"))

# table(top100stdSI %in% parents) # only 3
# length(grep("TMS13|TMS14|TMS15",top100stdSI, invert = T)) # 2
# length(grep("TMS13",top100stdSI)) # 52
# length(grep("TMS14",top100stdSI)) # 31
# length(grep("TMS15",top100stdSI)) # 15

## Highest BV -------------
# gebvs %>% 
#   slice_max(order_by = stdSI, n=1) # TMS13F1095P0013
## Lowest BV
# gebvs %>% 
#   filter(germplasmName %in% union(parents,top100stdSI)) %>% 
#   slice_min(order_by = stdSI, n=1) # IITA-TMS-IBA011371

# Crosses To Predict -------------
CrossesToPredict<-crosses2predict(union(parents,top100stdSI)) %>%  # makes df of pairwise non-recprical, selfs-included crosses
  bind_rows(ped %>% # add the crosses already made (for convenience)
              distinct(sireID,damID)) %>% 
  distinct # avoid duplication
# nrow(CrossesToPredict) # [1] 47083
saveRDS(CrossesToPredict,here::here("output","CrossesToPredict_top100stdSI_and_209originalParents.rds"))

Preducted untested cross variances

Model A and AD

Set-up

# activate multithread OpenBLAS
export OMP_NUM_THREADS=88;

library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);

# Crosses To Predict -------------
CrossesToPredict<-readRDS(here::here("output","CrossesToPredict_top100stdSI_and_209originalParents.rds"))

# Pedigree -----------
ped<-readRDS(here::here("data","ped_awc.rds")) %>%
  distinct(sireID,damID)
parents<-union(ped$sireID,ped$damID)
top100stdSI<-readRDS(here::here("output","top100stdSI.rds"))

# 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(parents,top100stdSI),"_HapA"),
                   paste0(union(parents,top100stdSI),"_HapB")))
haploMat<-haploMat[parenthaps,colnames(recombFreqMat)]; dim(haploMat)

# 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","getUntestedMtCrossVarPreds.R"))

# Divide CrossesToPredict into chunks for each server ------------
nchunks<-5
CrossesToPredict %<>% 
  mutate(Chunk=rep(1:nchunks, each=ceiling(nrow(.)/nchunks), length.out=nrow(.))) %>% 
  nest(data=c(sireID,damID))

Run Var(TGV) predictions (Model AD)

# cbsulm13 - done
chunk<-1;
# cbsulm15 - done
chunk<-2;
# cbsulm20 - done
chunk<-3;
# cbsulm22 - done
chunk<-4;
# cbsulm23 - done
chunk<-5;

# Start run on each server / chunk:  Aug 03 at 6:50AM
getUntestedMtCrossVarPreds(inprefix = "mt_All_AD",
                           outpath = "output/crossPredictions",
                           outprefix = paste0("predUntestedCrossTGVs_chunk",chunk,"_AD"),
                           predType="VPM", Model = "AD", nIter=30000, burnIn=5000,thin=5,
                           CrossesToPredict=CrossesToPredict$data[[chunk]],
                           recombFreqMat=recombFreqMat,haploMat=haploMat,ncores=ncores)

Run Var(BV) predictions (Model A)

# cbsulm13 - aug 4, 10pm - done
chunk<-1;
# cbsulm15 - aug 4, 10pm - done
chunk<-2;
# cbsulm20 - aug 4, 6:55am - done
chunk<-3;
# cbsulm22 - aug 4, 10pm - done 
chunk<-4;
# cbsulm23 - aug 4, 6:55am - done
chunk<-5;

# Start run on each server / chunk: 
getUntestedMtCrossVarPreds(inprefix = "mt_All_A",
                           outpath = "output/crossPredictions",
                           outprefix = paste0("predUntestedCrossBVs_chunk",chunk,"_A"),
                           predType="VPM", Model = "A", nIter=30000, burnIn=5000,thin=5,
                           CrossesToPredict=CrossesToPredict$data[[chunk]],
                           recombFreqMat=recombFreqMat,haploMat=haploMat,ncores=ncores)

Model DirDom

Set-up

# activate multithread OpenBLAS
export OMP_NUM_THREADS=112;

library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);

# Crosses To Predict -------------
CrossesToPredict<-readRDS(here::here("output","CrossesToPredict_top100stdSI_and_209originalParents.rds"))

# Pedigree -----------
ped<-readRDS(here::here("data","ped_awc.rds")) %>%
  distinct(sireID,damID)
parents<-union(ped$sireID,ped$damID)
top100stdSI<-readRDS(here::here("output","top100stdSI.rds"))

# 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(parents,top100stdSI),"_HapA"),
                   paste0(union(parents,top100stdSI),"_HapB")))
haploMat<-haploMat[parenthaps,colnames(recombFreqMat)]; dim(haploMat)

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

# 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","getUntestedMtCrossVarPreds.R"))

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

Run Var(TGV) predictions

# cbsulm13 - Done!
chunk<-1;
# cbsulm17 - Done!
chunk<-2;
# cbsulm12 - Done!
chunk<-3;
# cbsulm16 - Done!
chunk<-4;

# Start run on each server / chunk: 
getDirDomUntestedMtCrossVarTGVpreds(inprefix = "mt_All_DirectionalDom",
                                    outpath = "output/crossPredictions",
                                    outprefix = paste0("predUntestedCrossTGVs_chunk",chunk,"_DirDom"),
                                    predType="VPM", nIter=30000, burnIn=5000,thin=5,
                                    CrossesToPredict=CrossesToPredict$data[[chunk]],
                                    recombFreqMat=recombFreqMat,haploMat=haploMat,ncores=ncores)

Run Var(BV) predictions

# cbsulm26 - Done! (~32hrs)
chunk<-1;
# cbsulm15 - Done!
chunk<-2;
# cbsulm26 - Done!
chunk<-3;
# cbsulm17 - Done!
chunk<-4;

# Start run on each server / chunk: 
getDirDomUntestedMtCrossVarBVpreds(inprefix = "mt_All_DirectionalDom",
                                   outpath = "output/crossPredictions",
                                   outprefix = paste0("predUntestedCrossBVs_chunk",chunk,"_DirDom"),
                                   predType="VPM", nIter=30000, burnIn=5000,thin=5,
                                   CrossesToPredict=CrossesToPredict$data[[chunk]],
                                   recombFreqMat=recombFreqMat,haploMat=haploMat,doseMat=snps,ncores=ncores)

Re-predict self-crosses

Discovered a bug effecting self-crosses. The original version of predCrossVar run (circa July 2020) incorrectly calculated gametic LD matrices with duplicated haplotypes for cases where sireID==damID. Fixed the bug in the package, archived the original version via Git/GitHub. Re-install predCrossVar on server and re-do predictions of selfs.

Model A and AD

Set-up

# activate multithread OpenBLAS
export OMP_NUM_THREADS=88;

library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);

# Crosses To Predict -------------
CrossesToPredict<-readRDS(here::here("output","CrossesToPredict_top100stdSI_and_209originalParents.rds"))
# just the selfs
CrossesToPredict %<>% filter(sireID==damID)
# Pedigree -----------
ped<-readRDS(here::here("data","ped_awc.rds")) %>%
  distinct(sireID,damID)
parents<-union(ped$sireID,ped$damID)
top100stdSI<-readRDS(here::here("output","top100stdSI.rds"))

# 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(parents,top100stdSI),"_HapA"),
                   paste0(union(parents,top100stdSI),"_HapB")))
haploMat<-haploMat[parenthaps,colnames(recombFreqMat)]; dim(haploMat)

# 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","getUntestedMtCrossVarPreds.R"))

Run Var(TGV) predictions (Model AD)

# Start run on each server / chunk:  
getUntestedMtCrossVarPreds(inprefix = "mt_All_AD",
                           outpath = "output/crossPredictions",
                           outprefix = paste0("predUntestedCrossTGVs_ReDoSelfs_AD"),
                           predType="VPM", Model = "AD", 
                           nIter=30000, burnIn=5000,thin=5,
                           CrossesToPredict=CrossesToPredict,
                           recombFreqMat=recombFreqMat,
                           haploMat=haploMat,ncores=ncores)

Run Var(BV) predictions (Model A)

# Start run on each server / chunk: 
getUntestedMtCrossVarPreds(inprefix = "mt_All_A",
                           outpath = "output/crossPredictions",
                           outprefix = paste0("predUntestedCrossBVs_ReDoSelfs_A"),
                           predType="VPM", Model = "A", 
                           nIter=30000, burnIn=5000,thin=5,
                           CrossesToPredict=CrossesToPredict,
                           recombFreqMat=recombFreqMat,
                           haploMat=haploMat,ncores=ncores)

Predict untested cross means

Set-up

# activate multithread OpenBLAS
export OMP_NUM_THREADS=112;

library(tidyverse); library(magrittr); library(predCrossVar); library(BGLR);
# Crosses To Predict -------------
CrossesToPredict<-readRDS(here::here("output","CrossesToPredict_top100stdSI_and_209originalParents.rds"))

# Pedigree -----------
ped<-readRDS(here::here("data","ped_awc.rds")) %>%
  distinct(sireID,damID)
parents<-union(ped$sireID,ped$damID)
top100stdSI<-readRDS(here::here("output","top100stdSI.rds"))

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

# 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"

# getMtCrossMeanPreds function -------------
source(here::here("code","getMtCrossMeanPreds.R"))
# getDirectionalDomMtCrossMeanPreds function -------------
source(here::here("code","getDirectionalDomMtCrossMeanPreds.R"))

# sampleIDs -------
blups<-readRDS(here::here("data","blups_forawcdata.rds")) %>% 
  select(Trait,blups) %>% # BLUPs long-->wide for multivar analysis
  unnest(blups) %>% 
  select(Trait,germplasmName,drgBLUP) %>% 
  spread(Trait,drgBLUP) %>% 
  select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order
sampleIDs<-blups$germplasmName[blups$germplasmName %in% rownames(snps)]
rm(blups)

Run mean predictions (Model A and AD)

# Done Aug 8
predictedUntestedCrossMeansBV<-getMtCrossMeanPreds(outprefix="mt_All_A",
                                                   Model="A",
                                                   CrossesToPredict=CrossesToPredict,
                                                   doseMat=snps,
                                                   sampleIDs = sampleIDs)
predictedUntestedCrossMeansTGV<-getMtCrossMeanPreds(outprefix="mt_All_AD",
                                                    Model="AD",
                                                    CrossesToPredict=CrossesToPredict,
                                                    doseMat=snps,
                                                    sampleIDs = sampleIDs)

saveRDS(predictedUntestedCrossMeansBV,file=here::here("output/crossPredictions","predictedUntestedCrossMeansBV.rds"))
saveRDS(predictedUntestedCrossMeansTGV,file=here::here("output/crossPredictions","predictedUntestedCrossMeansTGV.rds"))

Run mean predictions (Model DirDom)

predictedUntestedCrossMeansDirDom<-getDirectionalDomMtCrossMeanPreds(outprefix="mt_All_DirectionalDom",
                                                                     CrossesToPredict=CrossesToPredict,
                                                                     doseMat=snps,
                                                                     sampleIDs = sampleIDs)

saveRDS(predictedUntestedCrossMeansDirDom,
        file=here::here("output/crossPredictions","predictedUntestedCrossMeansDirDom.rds"))

Process prediction results

Tidy predicted variances

Next step, used a server, unpacking output for 10 varcomps x ~47K crosses.

Keep the dom. variance in the “tidied” output long enough to calculate dom. variance on the sel. indices. This was previously not done.

Calc. VarTGV=VarA + VarD only for the sup. tables (Tables S17 and S18), which are used for plots and summaries in the manuscript.

library(tidyverse); library(magrittr); library(predCrossVar) 
# Model A
predUntestedCrossVarBVs<-list.files(here::here("output/crossPredictions")) %>% 
  grep("predUntestedCrossBVs",.,value = T) %>% 
  tibble(File=.) %>% 
  mutate(Model=ifelse(grepl("_A_",File),"A","DirDomBV"),
         crossPredictions=map(File,~readRDS(here::here("output/crossPredictions",.)))) %>% 
  unnest_wider(crossPredictions) %>% 
  unnest(varcovars) %>% 
  select(-totcomputetime) %>% 
  unnest_wider(varcomps) # 10 variances/covariances x 5 chunks x 1 models = 50 blocks of ~10K crosses chunk per varcomp

require(furrr); options(mc.cores=50); plan(multiprocess)
predUntestedCrossVarBVs %<>% # in parallel across the 50 chunks
  mutate(predictedfamvars=future_map(predictedfamvars,~unnest(.,predVars) %>% select(-PMV))) %>% 
  select(-totcomputetime) %>% 
  unnest(predictedfamvars) %>% 
  rename(predVar=VPM) %>% 
  mutate(predOf="VarBV")
predUntestedCrossVarBVs %<>% select(-VarComp,-totcomputetime)

library(tidyverse); library(magrittr); library(predCrossVar) 

# Model AD
predUntestedCrossVarTGVs<-list.files(here::here("output/crossPredictions")) %>% 
  grep("predUntestedCrossTGVs",.,value = T) %>% 
  tibble(File=.) %>% 
  mutate(Model=ifelse(grepl("_AD_",File),"AD","DirDomAD"),
         crossPredictions=map(File,~readRDS(here::here("output/crossPredictions",.)))) %>% 
  unnest_wider(crossPredictions) %>% 
  unnest(varcovars) %>% 
  select(-totcomputetime) %>% 
  unnest_wider(varcomps) # 10 variances/covariances x 5 chunks x 2 models = 100 blocks of ~10K crosses chunk per varcomp
predUntestedCrossVarTGVs$predictedfamvars[[1]] %>% 
  mutate(Nsegsnps=map_dbl(predVars,~.$Nsegsnps[1]))
require(furrr); options(mc.cores=50); plan(multiprocess)
predUntestedCrossVarTGVs %<>% # in parallel across the 100 chunks
  mutate(predictedfamvars=future_map(predictedfamvars,function(predictedfamvars){
    return(predictedfamvars %<>% 
             # format each families output in serial 
             mutate(Nsegsnps=map_dbl(predVars,~.$Nsegsnps[1]),
                    predVars=map(predVars,function(predVars){ 
                      return(predVars %>%
                               select(VarComp,VPM)) })) %>% 
             unnest(predVars))})) %>% 
  select(-totcomputetime) %>% 
  unnest(predictedfamvars) %>% 
  rename(predVar=VPM) %>% 
  rename(predOf=VarComp)

Remove the original predictions for selfs and replace with the correct predictions.

# verify nrow before removing "re-do's"
predUntestedCrossVarBVs %>% dim() # [1] 947780      9
# df of re-predicted selfs
## should only be from the ClassicAD model
redoBVs<-predUntestedCrossVarBVs %>% 
  filter(grepl("_ReDoSelfs_",File))
dim(redoBVs) # [1] 6120    9
redoBVs %>% count(Model,predOf)
#   Model predOf     n
#   <chr> <chr>  <int>
# 1 A     VarBV   3060

# df of original predictions (selfs and outcrosses)
# predUntestedCrossVarBVs %>% 
#   filter(!grepl("_ReDoSelfs_",File)) %>% # dim() # [1] 941660      9 # verify redo's removed
#   filter(sireID==damID) %>% # dim() # [1] 6120    9 original self's predictions remain at this point
predUntestedCrossVarBVs %<>% 
  # remove all selfs from the ClassicAD model
  filter(sireID!=damID | (sireID==damID & Model=="DirDomBV")) %>% # dim() # [1] 935540      9
  # add back the corrected predictions for selfs
  bind_rows(.,redoBVs)

redoTGVs<-predUntestedCrossVarTGVs %>% 
  filter(grepl("_ReDoSelfs_",File))
dim(redoTGVs) # [1] 12240   11
predUntestedCrossVarTGVs %<>% 
  # remove all selfs
  filter(sireID!=damID | (sireID==damID & Model=="DirDomAD")) %>% # dim() # [1] 1871080     11
  # add back the corrected predictions for selfs
  bind_rows(.,redoTGVs)
# dim(predUntestedCrossVarTGVs) # [1] 1883320       9

predUntestedCrossVars<-bind_rows(predUntestedCrossVarBVs,
                                 predUntestedCrossVarTGVs)
  # left_join(predUntestedCrossVarBVs %>% 
  #                                  rename(predVarBV=predVarA) %>% 
  #                                  select(-totcomputetime,-File),
  #                                predUntestedCrossVarTGVs %>% 
  #                                  rename(predVarTGV=predVarTot) %>% 
  #                                  select(-predVarA,-predVarD,-totcomputetime,-File)) %>% 
  # pivot_longer(cols=c(predVarBV,predVarTGV),
  #              names_to = "predOf",values_to = "predVar")
# predUntestedCrossVars<-bind_rows(predUntestedCrossVarBVs,
#                                  predUntestedCrossVarTGVs)
#predUntestedCrossVars %<>% select(Model,Trait1,Trait2,sireID,damID,predOf,predVar,Nsegsnps,totcomputetime)

saveRDS(predUntestedCrossVars,here::here("output/crossPredictions","predictedUntestedCrossVars_tidy_traits.rds"))

Tidy predicted means

library(tidyverse); library(magrittr); library(predCrossVar)
predictedUntestedCrossMeansBV<-readRDS(here::here("output/crossPredictions","predictedUntestedCrossMeansBV.rds")) %>%
  pluck("predictedCrossMeans")
predictedUntestedCrossMeansTGV<-readRDS(here::here("output/crossPredictions","predictedUntestedCrossMeansTGV.rds")) %>%
  pluck("predictedCrossMeans")
predictedUntestedCrossMeansDirDom<-readRDS(here::here("output/crossPredictions","predictedUntestedCrossMeansDirDom.rds")) %>%
  pluck("predictedCrossMeans")
predictedUntestedCrossMeans<-predictedUntestedCrossMeansBV %>% 
  left_join(predictedUntestedCrossMeansTGV) %>% 
  mutate(Model="ClassicAD") %>% 
  bind_rows(predictedUntestedCrossMeansDirDom %>% 
              mutate(Model="DirDom"))
saveRDS(predictedUntestedCrossMeans,here::here("output/crossPredictions","predictedUntestedCrossMeans_tidy_traits.rds"))

Compute predictions on SI

Our focus in evaluating predictions of untested crosses will be on predictions on the two SI.

predVarBV - Model A (ClassicAD)
predVarTGV - Model AD (ClassicAD)
predVarBV - DirDomA (DirDom)
predVarTGV - DirDomAD (DirDom)

times

stdSI
biofortSI

for predicted means and variances.

Variances

library(tidyverse); library(magrittr);
# Selection weights -----------
indices<-readRDS(file=here::here("data","selection_index_weights_4traits.rds"))

predictedUntestedCrossVars<-readRDS(here::here("output/crossPredictions","predictedUntestedCrossVars_tidy_traits.rds"))  
#  select(-predVarD,-totcomputetime)

## Predicted Index Variances
predictedUntestedCrossVars_SI<-predictedUntestedCrossVars %>% 
  # filter(Model=="A") %>% 
  # mutate(Model="ClassicAD") %>% 
  # rename(predVarBV=predVarA) %>% 
  # select(-predVarTot) %>% 
  # left_join(predictedUntestedCrossVars %>% 
  #             filter(Model=="ClassicAD") %>% 
  #             rename(predVarTGV=predVarTot) %>% 
  #             select(-predVarA)) %>% 
  # bind_rows(predictedUntestedCrossVars %>% 
  #             filter(Model=="DirDomAD") %>% 
  #             rename(predVarBV=predVarA,
  #                    predVarTGV=predVarTot)) %>% 
  # pivot_longer(cols=c(predVarBV,predVarTGV),names_to = "predOf",values_to = "predVar") %>% 
  nest(varcovars=c(Trait1,Trait2,predVar)) 
require(furrr); options(mc.cores=50); plan(multiprocess)
predictedUntestedCrossVars_SI %<>% 
  mutate(varcovars=future_map(varcovars,
                       function(varcovars){
                         # pairwise to square symmetric matrix
                         gmat<-varcovars %>% 
                           spread(Trait2,predVar) %>% 
                           column_to_rownames(var = "Trait1") %>% 
                           as.matrix %>% 
                           .[indices$Trait,indices$Trait]
                         gmat[lower.tri(gmat)]<-t(gmat)[lower.tri(gmat)]
                         return(gmat) }))

predictedUntestedCrossVars_SI %<>% 
  mutate(stdSI=future_map_dbl(varcovars,~t(indices$stdSI)%*%.%*%indices$stdSI),
         biofortSI=future_map_dbl(varcovars,~t(indices$biofortSI)%*%.%*%indices$biofortSI)) %>% 
  select(-varcovars)
predictedUntestedCrossVars_SI %<>% 
  pivot_longer(cols = c(stdSI,biofortSI),
               names_to = "Trait1", 
               values_to = "predVar") %>% 
  mutate(Trait2=Trait1)
#   pivot_wider(names_from = "predOf", values_from = "predVar")
# predictedUntestedCrossVars_SI %<>% 
#   pivot_longer(cols = c(predVarBV,predVarTGV), names_to = "predOf", values_to = "predVar")

predictedUntestedCrossVars_SI %<>% 
  select(Trait1,Trait2,sireID,damID,Nsegsnps,Model,predOf,predVar)
saveRDS(predictedUntestedCrossVars_SI,here::here("output/crossPredictions","predictedUntestedCrossVars_SelIndices.rds"))

Means

# Selection weights -----------
indices<-readRDS(file=here::here("data","selection_index_weights_4traits.rds"))
predictedUntestedCrossMeans<-readRDS(here::here("output/crossPredictions","predictedUntestedCrossMeans_tidy_traits.rds"))
predictedUntestedCrossMeans %<>% 
  pivot_longer(cols = c(sireGEBV,damGEBV,predMeanBV,predMeanGV), names_to = "predOf", values_to = "predMean") 

## Predicted Index Means
predictedUntestedCrossMeans %<>% 
  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")

predictedUntestedCrossMeans %<>% 
  pivot_wider(names_from = "predOf", values_from = "predMean") %>% 
  select(sireID,damID,Model,Trait,sireGEBV,damGEBV,predMeanBV,predMeanGV)

saveRDS(predictedUntestedCrossMeans,here::here("output/crossPredictions","predictedUntestedCrossMeans_SelIndices.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 Untested Cross Means and Variances

2020-July-30

Crosses to predict

Preducted untested cross variances

Model A and AD

Set-up

Run Var(TGV) predictions (Model AD)

Run Var(BV) predictions (Model A)

Model DirDom

Set-up

Run Var(TGV) predictions

Run Var(BV) predictions

Re-predict self-crosses

Model A and AD

Set-up

Run Var(TGV) predictions (Model AD)

Run Var(BV) predictions (Model A)

Predict untested cross means

Set-up

Run mean predictions (Model A and AD)

Run mean predictions (Model DirDom)

Process prediction results

Tidy predicted variances

Tidy predicted means

Compute predictions on SI

Variances

Means