Last updated: 2021-04-22

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Knit directory: BreedingSchemeOptGroup/

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Rmd 9df9d9b wolfemd 2021-04-22 Publish the initial files for the Breeding Scheme Optimization Group project

Preamble: Install AlphaSimHlpR

R packages we will need. Install them if necessary.

install.packages(c("tidyverse","AlphaSimR","devtools"))

Install AlphaSimHlpR

devtools::install_github("jeanlucj/AlphaSimHlpR", ref = 'master', 
                         dependencies = T, force = T) # force = T to ensure I get a fresh install

When prompted “Which would you like to update?” choose “1: All”.

library(AlphaSimHlpR)
# Get `Error: package ‘optiSel’ could not be loaded`??
install.packages("optiSel",dependencies = T)
library(AlphaSimHlpR)
# still error
library(optiSel)
# Error: package or namespace 
# load failed for ‘optiSel’: .on
# Load failed in loadNamespace() for 'rgl', 
# details: call:         rgl.init(initValue, onlyNULL) 
# error: OpenGL is not available in this build

Following is specific to my macOS install state

Google search of error leads to: https://stackoverflow.com/questions/9878693/error-in-loading-rgl-package-with-mac-os-x

Suggestion Solution: install XQuartz

brew install xquartz
library(AlphaSimHlpR)

Finally I get a clean load!

browseVignettes("AlphaSimHlpR")

The vignettes don’t show up… but their Rmd’s are in the GitHub Repo. Best guess: need to be added to the namespace or knit and the package master needs to be freshly built.

I downloaded the Rmd’s from GitHub here.

Basic AlphaSimHlpR tutorial

New R session. Follow the AlphaSimHlpR vignette.

I also had to download the inst folder and it’s example “control file” contents from GitHub here

# Make sure you have the right packages installed
neededPackages <- c("AlphaSimR", "dplyr", "tidyr", "plotrix", 
                    "lme4", "sommer", "optiSel")
for (p in neededPackages) if (!require(p, character.only=T)) install.packages(p)
Loading required package: AlphaSimR
Loading required package: R6
Loading required package: dplyr

Attaching package: 'dplyr'
The following object is masked from 'package:AlphaSimR':

    mutate
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
Loading required package: tidyr
Loading required package: plotrix
Loading required package: lme4
Loading required package: Matrix

Attaching package: 'Matrix'
The following objects are masked from 'package:tidyr':

    expand, pack, unpack
Loading required package: sommer
Loading required package: MASS

Attaching package: 'MASS'
The following object is masked from 'package:dplyr':

    select
Loading required package: lattice
Loading required package: crayon
Loading required package: optiSel
Warning in rgl.init(initValue, onlyNULL): RGL: unable to open X11 display
Warning: 'rgl.init' failed, running with 'rgl.useNULL = TRUE'.
suppressMessages(library(AlphaSimHlpR))

Define simulation settings

Define the genetic architecture of the population and other breeding scheme parameters in a list bsp.

bsp <- specifyPopulation(ctrlFileName="data/inst/PopulationCtrlFile_Small.txt")
bsp <- specifyPipeline(bsp, ctrlFileName="data/inst/ControlFile_Small.txt")
bsp <- specifyCosts(bsp, ctrlFileName="data/inst/CostsCtrlFile_Small.txt")
nReplications <- 3
bsp$nCyclesToRun <- 6

print(bsp)
$nChr
[1] 3

$effPopSize
[1] 100

$quickHaplo
[1] TRUE

$segSites
[1] 400

$nQTL
[1] 40

$nSNP
[1] 100

$genVar
[1] 40

$gxeVar
numeric(0)

$gxyVar
[1] 15

$gxlVar
[1] 10

$gxyxlVar
[1] 5

$meanDD
[1] 0.8

$varDD
[1] 0.01

$relAA
[1] 0.5

$nStages
[1] 3

$stageNames
[1] "SDN" "CET" "PYT"

$stageToGenotype
[1] "CET"

$trainingPopCycles
 F1 SDN CET PYT 
  0   3   3   2 

$nParents
[1] 15

$nCrosses
[1] 30

$nProgeny
[1] 10

$usePolycrossNursery
[1] FALSE

$nSeeds
[1] 300

$useOptContrib
[1] FALSE

$nCandOptCont
[1] 200

$targetEffPopSize
[1] 20

$nEntries
SDN CET PYT 
200  75  20 

$nReps
SDN CET PYT 
  1   1   2 

$nLocs
SDN CET PYT 
  1   2   2 

$nClonesToNCRP
[1] 3

$nChks
SDN CET PYT 
  5   4   2 

$entryToChkRatio
SDN CET PYT 
 50  25  20 

$errVars
SDN CET PYT 
200 100  70 

$phenoF1toStage1
[1] TRUE

$errVarPreStage1
[1] 500

$useCurrentPhenoTrain
[1] FALSE

$nCyclesToKeepRecords
[1] 4

$nCyclesToRun
[1] 6

$selCritPipeAdv
function (records, candidates, bsp, SP) 
{
    phenoDF <- framePhenoRec(records, bsp)
    if (!any(candidates %in% phenoDF$id)) {
        crit <- runif(length(candidates))
    }
    else {
        crit <- iidPhenoEval(phenoDF)
        crit <- crit[candidates]
    }
    names(crit) <- candidates
    return(crit)
}
<bytecode: 0x7f809a1aab38>
<environment: namespace:AlphaSimHlpR>

$selCritPopImprov
function (records, candidates, bsp, SP) 
{
    phenoDF <- framePhenoRec(records, bsp)
    if (!any(candidates %in% phenoDF$id)) {
        crit <- runif(length(candidates))
    }
    else {
        crit <- iidPhenoEval(phenoDF)
        crit <- crit[candidates]
    }
    names(crit) <- candidates
    return(crit)
}
<bytecode: 0x7f809a1aab38>
<environment: namespace:AlphaSimHlpR>

$analyzeInbreeding
[1] 0

$chkReps
SDN CET PYT 
  1   1   1 

$checks
NULL

$plotCosts
SDN CET PYT 
  1   8  14 

$perLocationCost
[1] 1000

$crossingCost
[1] 0.2

$qcGenoCost
[1] 1.5

$wholeGenomeCost
[1] 10

$develCosts
[1] 60

$genotypingCosts
  CET 
862.5 

$trialCosts
     [,1]
[1,] 2645

$locationCosts
[1] 2000

$totalCosts
       [,1]
[1,] 5567.5

Replicate simple scheme

Run a simple breeding scheme for 6 cycles

Replicate a very simple breeding program 3 times.

replicRecords <- lapply(1:nReplications, runBreedingScheme, 
                        nCycles=bsp$nCyclesToRun, 
                        initializeFunc=initFuncADChk, 
                        productPipeline=prodPipeFncChk, 
                        populationImprovement=popImprov1Cyc, bsp)
****** 1 
[1] "initFuncADChk deprecated. Please use initializeScheme"
1  [1] "prodPipeFncChk deprecated. Please use productPipeline"
2  [1] "prodPipeFncChk deprecated. Please use productPipeline"
3  [1] "prodPipeFncChk deprecated. Please use productPipeline"
4  [1] "prodPipeFncChk deprecated. Please use productPipeline"
5  [1] "prodPipeFncChk deprecated. Please use productPipeline"
6  [1] "prodPipeFncChk deprecated. Please use productPipeline"

****** 2 
[1] "initFuncADChk deprecated. Please use initializeScheme"
1  [1] "prodPipeFncChk deprecated. Please use productPipeline"
2  [1] "prodPipeFncChk deprecated. Please use productPipeline"
3  [1] "prodPipeFncChk deprecated. Please use productPipeline"
4  [1] "prodPipeFncChk deprecated. Please use productPipeline"
5  [1] "prodPipeFncChk deprecated. Please use productPipeline"
6  [1] "prodPipeFncChk deprecated. Please use productPipeline"

****** 3 
[1] "initFuncADChk deprecated. Please use initializeScheme"
1  [1] "prodPipeFncChk deprecated. Please use productPipeline"
2  [1] "prodPipeFncChk deprecated. Please use productPipeline"
3  [1] "prodPipeFncChk deprecated. Please use productPipeline"
4  [1] "prodPipeFncChk deprecated. Please use productPipeline"
5  [1] "prodPipeFncChk deprecated. Please use productPipeline"
6  [1] "prodPipeFncChk deprecated. Please use productPipeline"

Calculate means and plot results

Calculate the means of the breeding programs and plot them out

plotData <- plotRecords(replicRecords)

Version Author Date
fe3048a wolfemd 2021-04-22

Version Author Date
fe3048a wolfemd 2021-04-22

Version Author Date
fe3048a wolfemd 2021-04-22

Version Author Date
fe3048a wolfemd 2021-04-22 
meanMeans <- tapply(plotData$genValMean, list(plotData$year, plotData$stage), mean)
meanMeans <- meanMeans[,c("F1", bsp$stageNames)]
stdErrMeans <- tapply(plotData$genValMean, list(plotData$year, plotData$stage), std.error)
stdErrMeans <- stdErrMeans[,c("F1", bsp$stageNames)]
print(meanMeans)
         F1       SDN       CET       PYT
0  4.884124  3.719726  4.459117  5.992662
1  6.993365  6.040825  5.776279  8.348509
2  8.151032  7.705864  7.850151  9.413862
3  8.774025  8.939957  9.735685 11.019171
4 10.214759  9.632609 10.806788 12.763704
5 11.676611 10.816320 11.177347 14.040778
6 12.409212 12.361436 12.745762 14.584065
print(stdErrMeans)
         F1       SDN        CET        PYT
0 0.1149940 0.2880178 0.51996148 0.56538833
1 0.6021817 0.1578891 0.41483506 0.26419922
2 0.4611349 0.4427966 0.09911811 0.04757649
3 1.2025796 0.4219375 0.26979001 0.98601057
4 1.0475610 1.2193721 0.24522642 1.16266465
5 0.7402013 0.9328822 1.51289357 0.50244206
6 0.9863010 0.7740142 0.89443847 1.00791981

Next step

Run a simple example simulation of the effect of reducing error with new tools.


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] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] AlphaSimHlpR_0.2.0 MCMCpack_1.5-0     coda_0.19-4        optiSel_2.0.5     
 [5] sommer_4.1.3       crayon_1.4.1       lattice_0.20-41    MASS_7.3-53.1     
 [9] lme4_1.1-26        Matrix_1.3-2       plotrix_3.8-1      tidyr_1.1.3       
[13] dplyr_1.0.5        AlphaSimR_0.13.0   R6_2.5.0           workflowr_1.6.2   

loaded via a namespace (and not attached):
 [1] mcmc_0.9-7              nlme_3.1-152            matrixStats_0.58.0     
 [4] fs_1.5.0                kinship2_1.8.5          doParallel_1.0.16      
 [7] webshot_0.5.2           rprojroot_2.0.2         numDeriv_2016.8-1.1    
[10] tools_4.0.3             bslib_0.2.4             utf8_1.2.1             
[13] DBI_1.1.1               manipulateWidget_0.10.1 tidyselect_1.1.0       
[16] compiler_4.0.3          git2r_0.28.0            quantreg_5.85          
[19] pspline_1.0-18          SparseM_1.81            alabama_2015.3-1       
[22] cccp_0.2-7              sass_0.3.1              quadprog_1.5-8         
[25] pkgdown_1.6.1           stringr_1.4.0           digest_0.6.27          
[28] minqa_1.2.4             rmarkdown_2.7           pkgconfig_2.0.3        
[31] htmltools_0.5.1.1       highr_0.9               fastmap_1.1.0          
[34] htmlwidgets_1.5.3       rlang_0.4.10            shiny_1.6.0            
[37] jquerylib_0.1.3         generics_0.1.0          jsonlite_1.7.2         
[40] crosstalk_1.1.1         magrittr_2.0.1          ECOSolveR_0.5.4        
[43] Rcpp_1.0.6              fansi_0.4.2             abind_1.4-5            
[46] lifecycle_1.0.0         scatterplot3d_0.3-41    stringi_1.5.3          
[49] whisker_0.4             yaml_2.2.1              plyr_1.8.6             
[52] grid_4.0.3              promises_1.2.0.1        miniUI_0.1.1.1         
[55] splines_4.0.3           nadiv_2.17.1            knitr_1.32             
[58] pillar_1.6.0            boot_1.3-27             reshape2_1.4.4         
[61] codetools_0.2-18        magic_1.5-9             glue_1.4.2             
[64] evaluate_0.14           HaploSim_1.8.4          data.table_1.14.0      
[67] vctrs_0.3.7             nloptr_1.2.2.2          httpuv_1.5.5           
[70] optiSolve_0.1.2         foreach_1.5.1           MatrixModels_0.5-0     
[73] purrr_0.3.4             assertthat_0.2.1        cachem_1.0.4           
[76] xfun_0.22               mime_0.10               xtable_1.8-4           
[79] pedigree_1.4            later_1.1.0.1           conquer_1.0.2          
[82] minpack.lm_1.2-1        tibble_3.1.1            iterators_1.0.13       
[85] memoise_2.0.0           shapes_1.2.6            rgl_0.106.6            
[88] statmod_1.4.35          ellipsis_0.3.1