Compute prediction accuracies

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Last updated: 2021-01-03

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Knit directory: PredictOutbredCrossVar/

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    Deleted:    output/crossPredictions/predUntestedCrosses_top100stdSI_chunk2_AD_predVarsAndCovars.rds
    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
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    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
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    Modified:   output/mtMarkerEffects/mt_GG_A.rds
    Modified:   output/mtMarkerEffects/mt_GG_AD.rds
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    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
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    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
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    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
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    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold1_testset_DirectionalDom.rds
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    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold1_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold2_testset_A.rds
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    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
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    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold4_trainset_DirectionalDom.rds
    Modified:   output/mtMarkerEffects/mt_Repeat4_Fold5_testset_A.rds
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    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/predictionAccuracy.Rmd) and HTML (docs/predictionAccuracy.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	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.
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.
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.

---

Observed vs. Predicted

Format predicted and observed values so prediction accuracy can be computed.

Means

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

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

✓ ggplot2 3.3.2     ✓ purrr   0.3.4
✓ tibble  3.0.4     ✓ dplyr   1.0.2
✓ tidyr   1.1.2     ✓ stringr 1.4.0
✓ readr   1.4.0     ✓ forcats 0.5.0

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

Attaching package: 'magrittr'

The following object is masked from 'package:purrr':

    set_names

The following object is masked from 'package:tidyr':

    extract

predmeans<-readRDS(here::here("output/crossPredictions","predictedCrossMeans_tidy_withSelIndices.rds"))
predmeans_dd<-readRDS(here::here("output/crossPredictions","predictedCrossMeans_DirectionalDom_tidy_withSelIndices.rds"))
#predmeans %>% count(Model,predOf)
#predmeans_dd %>% count(predOf)
predmeans %<>% 
  bind_rows(predmeans_dd %>% 
              mutate(Model=ifelse(predOf=="MeanBV","DirDomBV","DirDomAD"))) %>% 
  #rename(VarComp=predOf) %>% 
  mutate(predOf=gsub("MeanGV","MeanTGV",predOf))
rm(predmeans_dd)
#predmeans %>% count(Model,VarComp)
obsMeans<-readRDS(here::here("output/crossRealizations","realizedCrossMeans.rds")) %>% 
  rename(predOf=obsOf) %>% 
  mutate(Model=ifelse(Model=="DirDom",
                      ifelse(predOf=="MeanBV","DirDomBV","DirDomAD"),
                      Model))
#obsMeans %>% count(Model,predOf)
obsMeanBLUPs<-readRDS(here::here("output/crossRealizations","realizedCrossMeans_BLUPs.rds"))

obsVSpredMeans<-bind_rows(left_join(predmeans,obsMeans) %>% mutate(ValidationData="GBLUPs"),
                          left_join(predmeans,obsMeanBLUPs) %>% mutate(ValidationData="iidBLUPs"))

Joining, by = c("Repeat", "Fold", "Model", "sireID", "damID", "predOf", "Trait")

Joining, by = c("sireID", "damID", "Trait")

# obsVSpredMeans %>% count(Model,ValidationData,VarComp) %>% spread(ValidationData,n)

Variances

# Variances
predvars<-readRDS(here::here("output/crossPredictions","predictedCrossVars_tidy_withSelIndices.rds")) %>% 
  bind_rows(readRDS(here::here("output/crossPredictions","predictedCrossVars_DirectionalDom_tidy_withSelIndices.rds"))) %>% 
  select(-Nsegsnps,-totcomputetime) %>% 
  pivot_longer(cols=c(VPM,PMV),names_to = "VarMethod",values_to = "predVar") %>% 
  group_by(Repeat,Fold,Model,sireID,damID,Trait1,Trait2,VarMethod) %>%  
  # sum over VarComps (ModelA = VarA, ModelAD = VarA+VarD)
  summarize(predVar=sum(predVar),.groups="drop") %>%  
  mutate(predOf=ifelse(Model %in% c("A","DirDomBV"),"VarBV",
                       ifelse(Model %in% c("AD","DirDomAD"),"VarTGV",NA))) 
predvars %>% 
  count(Model,predOf)

# A tibble: 4 x 3
  Model    predOf     n
  <chr>    <chr>  <int>
1 A        VarBV  99648
2 AD       VarTGV 99648
3 DirDomAD VarTGV 99648
4 DirDomBV VarBV  99648

obsVars<-readRDS(here::here("output/crossRealizations","realizedCrossVars.rds")) %>% 
  rename(predOf=obsOf) %>% 
  mutate(Model=ifelse(Model=="DirDom",
                      ifelse(predOf=="VarBV","DirDomBV","DirDomAD"),
                      Model))
obsVars %>% count(Model,predOf)

# A tibble: 4 x 3
  Model    predOf     n
  <chr>    <chr>  <int>
1 A        VarBV  40374
2 AD       VarTGV 40374
3 DirDomAD VarTGV 40374
4 DirDomBV VarBV  40374

obsVarBLUPs<-readRDS(here::here("output/crossRealizations","realizedCrossVars_BLUPs.rds"))

obsVSpredVars<-bind_rows(left_join(predvars,obsVars) %>% mutate(ValidationData="GBLUPs"),
                         left_join(predvars,obsVarBLUPs) %>% mutate(ValidationData="iidBLUPs"))

Joining, by = c("Repeat", "Fold", "Model", "sireID", "damID", "Trait1", "Trait2", "predOf")

Joining, by = c("sireID", "damID", "Trait1", "Trait2")

obsVSpredVars %>% count(Model,ValidationData,predOf)

# A tibble: 8 x 4
  Model    ValidationData predOf     n
  <chr>    <chr>          <chr>  <int>
1 A        GBLUPs         VarBV  99648
2 A        iidBLUPs       VarBV  99648
3 AD       GBLUPs         VarTGV 99648
4 AD       iidBLUPs       VarTGV 99648
5 DirDomAD GBLUPs         VarTGV 99648
6 DirDomAD iidBLUPs       VarTGV 99648
7 DirDomBV GBLUPs         VarBV  99648
8 DirDomBV iidBLUPs       VarBV  99648

Values for Weighted Corr

For ValidationData==“GBLUPs”, weight by the observed “FamSize”.

# add Family Sizes, for weighted correlations
obsVSpredVars %<>% 
  left_join(readRDS(file=here::here("output/crossRealizations","realizedCrossMetrics.rds")) %>% 
              distinct(Repeat,Fold,sireID,damID,FamSize) %>% ungroup())

Joining, by = c("Repeat", "Fold", "sireID", "damID")

obsVSpredVars %>% head

# A tibble: 6 x 13
  Repeat Fold  Model sireID damID Trait1 Trait2 VarMethod  predVar predOf
  <chr>  <chr> <chr> <chr>  <chr> <chr>  <chr>  <chr>        <dbl> <chr> 
1 Repea… Fold1 A     IITA-… IITA… biofo… biofo… PMV       55.4     VarBV 
2 Repea… Fold1 A     IITA-… IITA… biofo… biofo… VPM        5.86    VarBV 
3 Repea… Fold1 A     IITA-… IITA… DM     DM     PMV        4.35    VarBV 
4 Repea… Fold1 A     IITA-… IITA… DM     DM     VPM        0.355   VarBV 
5 Repea… Fold1 A     IITA-… IITA… DM     logFY… PMV       -0.0762  VarBV 
6 Repea… Fold1 A     IITA-… IITA… DM     logFY… VPM       -0.00459 VarBV 
# … with 3 more variables: obsVar <dbl>, ValidationData <chr>, FamSize <dbl>

For ValidationData==“iidBLUPs”, weight by the number of observed non-missing BLUPs per family per trait.

parentfolds<-readRDS(file = here::here("data","parentwise_crossVal_folds.rds")) %>% 
  rename(Repeat=id,Fold=id2) %>% 
  select(Repeat,Fold,testparents)
ped<-readRDS(here::here("data","ped_awc.rds")) %>%
  nest(FamilyMembers=FullSampleName)
parentfolds %<>% 
  mutate(CrossesToPredict=map(testparents,~filter(ped,sireID %in% . | damID %in% .))) %>% 
  select(-testparents)
indices<-readRDS(file=here::here("data","selection_index_weights_4traits.rds"))
blups<-readRDS(here::here("data","blups_forawcdata.rds")) %>% 
  select(Trait,blups) %>% 
  unnest(blups) %>% 
  select(Trait,germplasmName,BLUP) %>% 
  spread(Trait,BLUP) %>%  
  select(germplasmName,all_of(c("DM","logFYLD","MCMDS","TCHART"))) # precaution to ensure consistent column order
crossblups<-parentfolds %>% 
  unnest(CrossesToPredict) %>% 
  distinct(sireID,damID,FamilyMembers) %>% 
  unnest(FamilyMembers) %>% 
  rename(germplasmName=FullSampleName) %>% 
  left_join(blups) %>% 
  select(sireID,damID,germplasmName,all_of(indices$Trait)) %>% 
  nest(famblups=c(germplasmName,all_of(indices$Trait))) %>% 
  mutate(stdSI=map(famblups,~as.data.frame(.) %>% 
                       column_to_rownames(var = "germplasmName") %>% 
                       as.matrix(.)%*%indices$stdSI),
         biofortSI=map(famblups,~as.data.frame(.) %>% 
                       column_to_rownames(var = "germplasmName") %>% 
                       as.matrix(.)%*%indices$biofortSI))

Joining, by = "germplasmName"

nObs<-bind_rows(crossblups %>% 
                  select(-famblups) %>% 
                  mutate(stdSI=map_dbl(stdSI,~length(which(!is.na(.)))),
                         biofortSI=map_dbl(biofortSI,~length(which(!is.na(.))))) %>% 
                  pivot_longer(cols = c(stdSI,biofortSI), names_to = "Trait1", values_to = "Nobs",values_drop_na = TRUE) %>% 
                  mutate(Trait2=Trait1),
                crossblups %>% 
                  select(sireID,damID,famblups) %>% 
                  mutate(famblups=map(famblups,function(famblups){
                    NobsMat<-psych::pairwiseCount(famblups %>% select(-germplasmName),diagonal=TRUE)
                    NobsMat[lower.tri(NobsMat, diag = F)]<-NA
                    NobsMat %<>% 
                      as.data.frame %>% 
                      rownames_to_column(var = "Trait1") %>% 
                      pivot_longer(cols = -Trait1, names_to = "Trait2", values_to = "Nobs",values_drop_na = TRUE)
                    return(NobsMat) })) %>% 
                  unnest(famblups))
rm(parentfolds,ped,indices,blups,crossblups)
# add N obs, for weighted correlations
obsVSpredVars %<>%
  left_join(nObs) %>%
  mutate(CorrWeight=ifelse(ValidationData=="GBLUPs",FamSize,Nobs))

Joining, by = c("sireID", "damID", "Trait1", "Trait2")

Usefulness

# Usefulness
realizedcrossmetrics<-readRDS(file=here::here("output/crossRealizations","realizedCrossMetrics.rds"))
## Join the predicted means and variances
## Only for Sel Indices
#predvars %>% count(Model,predOf)
#predmeans %>%  count(Model,predOf)
predUsefulness<-left_join(predvars %>% # Variances
                            filter(Trait1 %in% c("stdSI","biofortSI"),
                                   Trait1==Trait2) %>% 
                            rename(Trait=Trait1) %>% 
                            select(-Trait2) %>% 
                            mutate(predOf=gsub("Var","",predOf)),
                          predmeans %>% # Means
                            filter(Trait %in% c("stdSI","biofortSI")) %>% 
                            mutate(predOf=gsub("Mean","",predOf))) %>% 
  mutate(predSD=sqrt(predVar)) %>% 
  select(-predVar)

Joining, by = c("Repeat", "Fold", "Model", "sireID", "damID", "Trait", "predOf")

## Add the realized selection intensities
## Create a variable "Stage" for which there are several applying to "Usefulness" for TGV
predBVs<-predUsefulness %>% 
  filter(predOf=="BV") %>% 
  left_join(realizedcrossmetrics %>% 
              select(Repeat,Fold,Model,sireID,damID,FamSize,realIntensityParent) %>% 
              rename(realIntensity=realIntensityParent) %>% 
              mutate(Stage="Parent",
                     Model=ifelse(Model=="ClassicAD","A","DirDomBV")))

Joining, by = c("Repeat", "Fold", "Model", "sireID", "damID")

predTGVs<-predUsefulness %>% 
  filter(predOf=="TGV") %>% 
  left_join(realizedcrossmetrics %>% 
              select(Repeat,Fold,Model,sireID,damID,FamSize,contains("realIntensity"),-realIntensityParent) %>% 
              pivot_longer(cols = contains("realIntensity"),
                           names_to = "Stage", 
                           values_to = "realIntensity", 
                           names_prefix = "realIntensity") %>% 
              mutate(Model=ifelse(Model=="ClassicAD","AD","DirDomAD")))

Joining, by = c("Repeat", "Fold", "Model", "sireID", "damID")

predUsefulness<-bind_rows(predBVs,
                          predTGVs) %>% 
  # include a "stage" (=="ConstIntensity") 
  # where intensity for predicted UC is set to 2.67
  bind_rows(predUsefulness %>% 
              left_join(realizedcrossmetrics %>% 
                          distinct(Repeat,Fold,sireID,damID,FamSize)) %>% 
              mutate(Stage="ConstIntensity",
                     realIntensity=2.67))

Joining, by = c("Repeat", "Fold", "sireID", "damID")

## Compute predicted UCs
predUsefulness %<>% 
  dplyr::mutate(predUC=predMean+(realIntensity*predSD))
predUsefulness %>% count(Model,predOf,Stage)

# A tibble: 14 x 4
   Model    predOf Stage              n
   <chr>    <chr>  <chr>          <int>
 1 A        BV     ConstIntensity 16608
 2 A        BV     Parent         16608
 3 AD       TGV    AYT            16608
 4 AD       TGV    CET            16608
 5 AD       TGV    ConstIntensity 16608
 6 AD       TGV    PYT            16608
 7 AD       TGV    UYT            16608
 8 DirDomAD TGV    AYT            16608
 9 DirDomAD TGV    CET            16608
10 DirDomAD TGV    ConstIntensity 16608
11 DirDomAD TGV    PYT            16608
12 DirDomAD TGV    UYT            16608
13 DirDomBV BV     ConstIntensity 16608
14 DirDomBV BV     Parent         16608

predUsefulness %>% filter(!is.na(predUC)) %>% count(Model,predOf,Stage)

# A tibble: 14 x 4
   Model    predOf Stage              n
   <chr>    <chr>  <chr>          <int>
 1 A        BV     ConstIntensity 16608
 2 A        BV     Parent          1732
 3 AD       TGV    AYT             3728
 4 AD       TGV    CET            14892
 5 AD       TGV    ConstIntensity 16608
 6 AD       TGV    PYT            10328
 7 AD       TGV    UYT             1412
 8 DirDomAD TGV    AYT             3728
 9 DirDomAD TGV    CET            14892
10 DirDomAD TGV    ConstIntensity 16608
11 DirDomAD TGV    PYT            10328
12 DirDomAD TGV    UYT             1412
13 DirDomBV BV     ConstIntensity 16608
14 DirDomBV BV     Parent          1732

## Format observed UCs
obsUCgca<-realizedcrossmetrics %>% 
  select(Repeat,Fold,Model,sireID,damID,contains("realizedUCparent")) %>% 
  pivot_longer(cols = contains("realizedUCparent"),
               names_to = "Trait", 
               values_to = "obsUC", 
               names_prefix = "realizedUCparent_",
               values_drop_na = T) %>% 
  mutate(predOf="BV",
         Stage="Parent")
#obsUCgca %>% count(VarComp,Stage,Model,Trait)
obsUCtgv<-realizedcrossmetrics %>% 
  select(Repeat,Fold,Model,sireID,damID,contains("realizedUCat")) %>% 
  pivot_longer(cols = contains("realizedUCat"),
               names_to = "Trait", 
               values_to = "obsUC", 
               names_prefix = "realizedUCat",
               values_drop_na = T) %>% 
  separate(Trait,c("Stage","Trait"),"_") %>% 
  mutate(predOf="TGV")
#obsUCtgv %>% count(VarComp,Stage,Model)
obsUsefulness<-bind_rows(obsUCgca,obsUCtgv)
obsUsefulness %<>% 
  bind_rows(realizedcrossmetrics %>% 
              select(Repeat,Fold,Model,sireID,damID,contains("meanTop1pctGEBV")) %>% 
              pivot_longer(cols = contains("meanTop1pctGEBV"),
                           names_to = "Trait", 
                           values_to = "obsUC",
                           names_prefix = "meanTop1pctGEBV_",
                           values_drop_na = T) %>% 
              mutate(predOf="BV",
                     Stage="ConstIntensity")) %>% 
  bind_rows(realizedcrossmetrics %>% 
              select(Repeat,Fold,Model,sireID,damID,contains("meanTop1pctGETGV")) %>% 
              pivot_longer(cols = contains("meanTop1pctGETGV"),
                           names_to = "Trait", 
                           values_to = "obsUC",
                           names_prefix = "meanTop1pctGETGV_",
                           values_drop_na = T) %>% 
              mutate(predOf="TGV",
                     Stage="ConstIntensity"))
obsUsefulness %<>% 
  mutate(Model=ifelse(Model=="ClassicAD",
                      ifelse(predOf=="BV","A","AD"),
                      ifelse(predOf=="BV","DirDomBV","DirDomAD")))
# obsUsefulness
# predUsefulness %>% count(Model,predOf,Stage)
# obsUsefulness %>% count(Model,predOf,Stage)
# obsUsefulness %>% filter(!is.na(obsUC))
# predUsefulness %>% filter(!is.na(predUC)) %>% count(Model,predOf,Stage)
#predUsefulness %>% filter(is.na(FamSize)) %>% count(Model,predOf,VarMethod,Stage)

obsVSpredUC<-left_join(predUsefulness,obsUsefulness) %>% ungroup()

Joining, by = c("Repeat", "Fold", "Model", "sireID", "damID", "Trait", "predOf", "Stage")

obsVSpredUC %<>% drop_na(.) %>% ungroup()
obsVSpredUC %>% str

tibble [130,616 × 15] (S3: tbl_df/tbl/data.frame)
 $ Repeat       : chr [1:130616] "Repeat1" "Repeat1" "Repeat1" "Repeat1" ...
 $ Fold         : chr [1:130616] "Fold1" "Fold1" "Fold1" "Fold1" ...
 $ Model        : chr [1:130616] "A" "A" "A" "A" ...
 $ sireID       : chr [1:130616] "IITA-TMS-IBA030075" "IITA-TMS-IBA030075" "IITA-TMS-IBA030075" "IITA-TMS-IBA030075" ...
 $ damID        : chr [1:130616] "IITA-TMS-IBA940006" "IITA-TMS-IBA940006" "IITA-TMS-IBA940006" "IITA-TMS-IBA940006" ...
 $ Trait        : chr [1:130616] "biofortSI" "biofortSI" "stdSI" "stdSI" ...
 $ VarMethod    : chr [1:130616] "PMV" "VPM" "PMV" "VPM" ...
 $ predOf       : chr [1:130616] "BV" "BV" "BV" "BV" ...
 $ predMean     : num [1:130616] 4.49 4.49 9.21 9.21 -1.03 ...
 $ predSD       : num [1:130616] 7.26 2.29 8.82 3.03 7.34 ...
 $ FamSize      : num [1:130616] 38 38 38 38 7 7 7 7 28 28 ...
 $ realIntensity: num [1:130616] 2.32 2.32 2.32 2.32 1.58 ...
 $ Stage        : chr [1:130616] "Parent" "Parent" "Parent" "Parent" ...
 $ predUC       : num [1:130616] 21.33 9.81 29.65 16.23 10.57 ...
 $ obsUC        : num [1:130616] -0.886 -0.886 7.152 7.152 0.163 ...

#obsVSpredUC %>% count(Model,predOf,Stage)

–> Save

saveRDS(obsVSpredMeans,here::here("output","obsVSpredMeans.rds"))
saveRDS(obsVSpredVars,here::here("output","obsVSpredVars.rds"))
saveRDS(obsVSpredUC,here::here("output","obsVSpredUC.rds"))

Compute prediction accuracies

rm(list=ls())
library(tidyverse); library(magrittr);
obsVSpredMeans<-readRDS(here::here("output","obsVSpredMeans.rds"))
obsVSpredVars<-readRDS(here::here("output","obsVSpredVars.rds"))
obsVSpredUC<-readRDS(here::here("output","obsVSpredUC.rds"))

# Means
obsVSpredMeans %<>%
  drop_na(.) %>% 
  nest(predVSobs=c(sireID,damID,predMean,obsMean)) %>% 
  mutate(Accuracy=map_dbl(predVSobs,~cor(.$predMean,.$obsMean,use = 'complete.obs'))) %>% 
  select(-predVSobs)

# Variances
obsVSpredVars %<>% 
  drop_na(.) %>% 
  select(-FamSize,-Nobs) %>% 
  nest(predVSobs=c(sireID,damID,predVar,obsVar,CorrWeight)) %>% 
  mutate(AccuracyWtCor=map_dbl(predVSobs,~psych::cor.wt(.[,3:4],w = .$CorrWeight) %$% r[1,2]),
         AccuracyCor=map_dbl(predVSobs,~cor(.$predVar,.$obsVar,use = 'complete.obs'))) %>% 
  select(-predVSobs)

# Usefulness
obsVSpredUC %<>% 
  select(-predMean,-predSD,-realIntensity) %>% 
  nest(predVSobs=c(sireID,damID,predUC,obsUC,FamSize)) %>% 
  mutate(AccuracyWtCor=map_dbl(predVSobs,~psych::cor.wt(.[,3:4],w = .$FamSize) %$% r[1,2]),
         AccuracyCor=map_dbl(predVSobs,~cor(.$predUC,.$obsUC,use = 'complete.obs'))) %>% 
  select(-predVSobs)
obsVSpredUC %>% count(Model,predOf,Stage)

# A tibble: 14 x 4
   Model    predOf Stage              n
   <chr>    <chr>  <chr>          <int>
 1 A        BV     ConstIntensity   100
 2 A        BV     Parent           100
 3 AD       TGV    AYT              100
 4 AD       TGV    CET              100
 5 AD       TGV    ConstIntensity   100
 6 AD       TGV    PYT              100
 7 AD       TGV    UYT              100
 8 DirDomAD TGV    AYT              100
 9 DirDomAD TGV    CET              100
10 DirDomAD TGV    ConstIntensity   100
11 DirDomAD TGV    PYT              100
12 DirDomAD TGV    UYT              100
13 DirDomBV BV     ConstIntensity   100
14 DirDomBV BV     Parent           100

–> Save

saveRDS(obsVSpredMeans,here::here("output","accuraciesMeans.rds"))
saveRDS(obsVSpredVars,here::here("output","accuraciesVars.rds"))
saveRDS(obsVSpredUC,here::here("output","accuraciesUC.rds"))

Session information

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] predCrossVar_0.1.0 magrittr_2.0.1     forcats_0.5.0      stringr_1.4.0     
 [5] dplyr_1.0.2        purrr_0.3.4        readr_1.4.0        tidyr_1.1.2       
 [9] tibble_3.0.4       ggplot2_3.3.2      tidyverse_1.3.0    workflowr_1.6.2   

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5        lattice_0.20-41   lubridate_1.7.9.2 here_1.0.1       
 [5] assertthat_0.2.1  rprojroot_2.0.2   digest_0.6.27     psych_2.0.9      
 [9] utf8_1.1.4        R6_2.5.0          cellranger_1.1.0  backports_1.2.1  
[13] reprex_0.3.0      evaluate_0.14     httr_1.4.2        pillar_1.4.7     
[17] rlang_0.4.9       readxl_1.3.1      rstudioapi_0.13   whisker_0.4      
[21] rmarkdown_2.6     munsell_0.5.0     broom_0.7.2       compiler_4.0.2   
[25] httpuv_1.5.4      modelr_0.1.8      xfun_0.19         pkgconfig_2.0.3  
[29] mnormt_2.0.2      tmvnsim_1.0-2     htmltools_0.5.0   tidyselect_1.1.0 
[33] fansi_0.4.1       crayon_1.3.4      dbplyr_2.0.0      withr_2.3.0      
[37] later_1.1.0.1     grid_4.0.2        nlme_3.1-151      jsonlite_1.7.2   
[41] gtable_0.3.0      lifecycle_0.2.0   DBI_1.1.0         git2r_0.27.1     
[45] scales_1.1.1      cli_2.2.0         stringi_1.5.3     fs_1.5.0         
[49] promises_1.1.1    xml2_1.3.2        ellipsis_0.3.1    generics_0.1.0   
[53] vctrs_0.3.5       tools_4.0.2       glue_1.4.2        hms_0.5.3        
[57] parallel_4.0.2    yaml_2.2.1        colorspace_2.0-0  rvest_0.3.6      
[61] knitr_1.30        haven_2.3.1