Last updated: 2021-08-27

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Knit directory: BreedingSchemeOpt/

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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
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

$selCritPipeAdv
function(records, candidates, bsp, SP){
  phenoDF <- framePhenoRec(records, bsp)
  # Candidates don't have phenotypes so return random vector
  if (!any(candidates %in% phenoDF$id)){
    crit <- runif(length(candidates))
  } else{
    crit <- iidPhenoEval(phenoDF)
    crit <- crit[candidates]
  }
  names(crit) <- candidates
  return(crit)
}
<bytecode: 0x7fae9fe59a40>
<environment: namespace:AlphaSimHlpR>

$selCritPopImprov
function(records, candidates, bsp, SP){
  phenoDF <- framePhenoRec(records, bsp)
  # Candidates don't have phenotypes so return random vector
  if (!any(candidates %in% phenoDF$id)){
    crit <- runif(length(candidates))
  } else{
    crit <- iidPhenoEval(phenoDF)
    crit <- crit[candidates]
  }
  names(crit) <- candidates
  return(crit)
}
<bytecode: 0x7fae9fe59a40>
<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  4.788684  3.384214  3.435910  5.784658
1  6.776836  5.591508  5.605060  6.483341
2  7.695134  7.481067  7.393820  9.799798
3  9.214746  8.525191  9.620269 10.129217
4  9.482519 10.021769 10.581698 12.783492
5 11.339604 10.248401 11.430718 14.049004
6 11.932867 11.979959 12.009061 13.983051
print(stdErrMeans)
         F1       SDN       CET       PYT
0 0.1294490 0.3511957 0.4192225 0.5054042
1 0.6598288 0.1100316 0.3948318 0.4281404
2 0.5907782 0.5954389 0.3311990 0.4274365
3 0.4103964 0.5907788 0.7397421 0.4224925
4 0.3596464 0.5870375 0.8675606 0.7937926
5 0.8101920 0.5019150 0.7412440 0.7320315
6 0.3171822 0.6888787 0.3588681 0.2304982

Next step

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


sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/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] AlphaSimHlpR_0.2.1 optiSel_2.0.5      sommer_4.1.4       crayon_1.4.1      
 [5] lattice_0.20-44    MASS_7.3-54        lme4_1.1-27.1      Matrix_1.3-4      
 [9] plotrix_3.8-1      tidyr_1.1.3        dplyr_1.0.7        AlphaSimR_1.0.3   
[13] R6_2.5.0           workflowr_1.6.2   

loaded via a namespace (and not attached):
 [1] magic_1.5-9          sass_0.4.0           foreach_1.5.1       
 [4] jsonlite_1.7.2       splines_4.1.0        ECOSolveR_0.5.4     
 [7] cccp_0.2-7           bslib_0.2.5.1        assertthat_0.2.1    
[10] highr_0.9            yaml_2.2.1           numDeriv_2016.8-1.1 
[13] pillar_1.6.2         glue_1.4.2           quadprog_1.5-8      
[16] alabama_2015.3-1     optiSolve_0.1.2      digest_0.6.27       
[19] promises_1.2.0.1     minqa_1.2.4          htmltools_0.5.1.1   
[22] httpuv_1.6.1         plyr_1.8.6           pkgconfig_2.0.3     
[25] purrr_0.3.4          whisker_0.4          later_1.2.0         
[28] git2r_0.28.0         shapes_1.2.6         tibble_3.1.3        
[31] generics_0.1.0       ellipsis_0.3.2       pedigree_1.4        
[34] cachem_1.0.5         magrittr_2.0.1       memoise_2.0.0       
[37] evaluate_0.14        kinship2_1.8.5       fs_1.5.0            
[40] fansi_0.5.0          doParallel_1.0.16    nlme_3.1-152        
[43] tools_4.1.0          data.table_1.14.0    HaploSim_1.8.4      
[46] minpack.lm_1.2-1     lifecycle_1.0.0      pspline_1.0-18      
[49] stringr_1.4.0        compiler_4.1.0       pkgdown_1.6.1       
[52] jquerylib_0.1.4      rlang_0.4.11         grid_4.1.0          
[55] nloptr_1.2.2.2       iterators_1.0.13     htmlwidgets_1.5.3   
[58] crosstalk_1.1.1      rmarkdown_2.10       boot_1.3-28         
[61] nadiv_2.17.1         codetools_0.2-18     abind_1.4-5         
[64] DBI_1.1.1            reshape2_1.4.4       knitr_1.33          
[67] fastmap_1.1.0        utf8_1.2.2           rprojroot_2.0.2     
[70] stringi_1.7.3        parallel_4.1.0       Rcpp_1.0.7          
[73] vctrs_0.3.8          rgl_0.107.10         scatterplot3d_0.3-41
[76] tidyselect_1.1.1     xfun_0.25