Last updated: 2021-04-22
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Knit directory: BreedingSchemeOptGroup/
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AlphaSimHlpR first steps: Installation and quickly running the AlphaSimHlpR tutorial example.
Generate a baseline population. As this is an example, I won’t use empirical inputs. Ignore costs for now.
BaselineControlFile.txt and BaselinePopulationFile.txt in this repository.I want to set-up “burn-in” generations of phenotypic selection. See if I can work that in AlphaSimHlpR.
SPOILER ALERT: Not quite working yet. More work needed.
The first step is to read in the control files defining the baseline breeding program and other breeding scheme parameters in a list bsp.
The tutorial example repeatedly runs the runBreedingScheme() function, which in turn depends on specified functions: initializeFunc=initFuncADChk, productPipeline=prodPipeFncChk, populationImprovement=popImprov1Cyc.
selCritPipeAdv and selCritPopImprov are set to selCritIID.
Now, to continue, we can’t use the runBreedingScheme() function because it will re-run the initializeFunc() and subsequently runMacs2(), which overwrites our founder haplotypes.
Make my own function: runBreedingScheme_wBurnIn().
Specify number of burn-in and post-burn in cycles.
Also let’s uses two separate bsp objects, one for before, another for after. Use with caution.
rm(list=ls()); gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 713178 38.1 1331791 71.2 NA 1331791 71.2
Vcells 1324981 10.2 8388608 64.0 65536 1981424 15.2library(tidyverse); library(magrittr); ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──✓ ggplot2 3.3.3 ✓ purrr 0.3.4
✓ tibble 3.1.1 ✓ dplyr 1.0.5
✓ tidyr 1.1.3 ✓ stringr 1.4.0
✓ readr 1.4.0 ✓ forcats 0.5.1── 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_namesThe following object is masked from 'package:tidyr':
extractsuppressMessages(library(AlphaSimHlpR))Warning in rgl.init(initValue, onlyNULL): RGL: unable to open X11 displayWarning: 'rgl.init' failed, running with 'rgl.useNULL = TRUE'.bsp <- specifyPopulation(ctrlFileName="data/BaselinePopulationFile.txt")
bsp <- specifyPipeline(bsp, ctrlFileName="data/BaselineControlFile.txt")runBreedingScheme_wBurnIn <- function(replication=NULL,
nBurnInCycles=10,nPostBurnInCycles=10,
bspBurnIn,bspPostBurnIn,
initializeFunc,
productPipeline,
populationImprovement){
cat("******", replication, "\n")
# This initiates the founding population
initList <- initializeFunc(bspBurnIn)
SP <- initList$SP
bspBurnIn <- initList$bsp
records <- initList$records
# Burn-in cycles
for (cycle in 1:nBurnInCycles){
cat(cycle, " ")
records <- productPipeline(records, bspBurnIn, SP)
records <- populationImprovement(records, bspBurnIn, SP)
}
# records <- AlphaSimHlpR:::lastCycStgOut(records, bspPostBurnIn, SP)
cat("\n")
cat("Burn-in Cycles")
cat("\n")
# Post burn-in cycles
for (cycle in (nBurnInCycles+1):(nBurnInCycles+nPostBurnInCycles)){
cat(cycle, " ")
records <- productPipeline(records, bspPostBurnIn, SP)
records <- populationImprovement(records, bspPostBurnIn, SP)
}
cat("\n")
# Finalize the stageOutputs
records <- AlphaSimHlpR:::lastCycStgOut(records, bspPostBurnIn, SP)
return(list(records=records,
bspBurnIn=bspBurnIn,
bspPostBurnIn=bspPostBurnIn,
SP=SP))
}For burn-in, using phenotypic selection, so the bsp already created.
For post burn-in, change selCritPopImprov to selCritGRM, but ignore selCritPipeAdv for now.
bspBurnIn<-bsp
bspPostBurnIn<-bspBurnIn
bspPostBurnIn[["selCritPopImprov"]] <- selCritGRM
test_sim<-runBreedingScheme_wBurnIn(replication = 1,
nBurnInCycles=2,nPostBurnInCycles=2,
bspBurnIn=bspBurnIn,
bspPostBurnIn=bspPostBurnIn,
initializeFunc=initializeScheme,
productPipeline=productPipeline,
populationImprovement=popImprov1Cyc)Unfortunately, haven’t gotten this to work yet. It seems likely more tweaking to the package is necessary.
I traced the problem at least as far as the populationImprovement() function in the post-burn-in phase and the makeGRM().
Set-up an experiment. No burn-in simulations.
# rm(list=ls()); gc()
# library(tidyverse); library(magrittr);
# suppressMessages(library(AlphaSimHlpR))
# bsp <- specifyPopulation(ctrlFileName="BaselinePopulationFile.txt")
# bsp <- specifyPipeline(bsp, ctrlFileName="BaselineControlFile.txt")# test<-runBreedingScheme(replication = 1, nCycles = 5, # just 5 for now
# initializeFunc=initializeScheme,
# productPipeline=prodPipeFncChk,
# populationImprovement=popImprov1Cyc, bsp)
#
# test_burnin$records$stageOutputs %>% ggplot(.,aes(x=year,y=genValMean, color=stage)) + geom_line()
# test_burnin$records
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 AlphaSimR_0.13.0
[13] R6_2.5.0 magrittr_2.0.1 forcats_0.5.1 stringr_1.4.0
[17] dplyr_1.0.5 purrr_0.3.4 readr_1.4.0 tidyr_1.1.3
[21] tibble_3.1.1 ggplot2_3.3.3 tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] minqa_1.2.4 colorspace_2.0-0 ellipsis_0.3.1
[4] rprojroot_2.0.2 fs_1.5.0 rstudioapi_0.13
[7] MatrixModels_0.5-0 fansi_0.4.2 lubridate_1.7.10
[10] xml2_1.3.2 codetools_0.2-18 splines_4.0.3
[13] doParallel_1.0.16 pedigree_1.4 cachem_1.0.4
[16] knitr_1.32 shapes_1.2.6 optiSolve_0.1.2
[19] jsonlite_1.7.2 kinship2_1.8.5 nloptr_1.2.2.2
[22] mcmc_0.9-7 broom_0.7.6 dbplyr_2.1.1
[25] shiny_1.6.0 compiler_4.0.3 httr_1.4.2
[28] backports_1.2.1 assertthat_0.2.1 fastmap_1.1.0
[31] cli_2.4.0 later_1.1.0.1 quantreg_5.85
[34] htmltools_0.5.1.1 tools_4.0.3 gtable_0.3.0
[37] glue_1.4.2 reshape2_1.4.4 Rcpp_1.0.6
[40] cellranger_1.1.0 jquerylib_0.1.3 pkgdown_1.6.1
[43] vctrs_0.3.7 nlme_3.1-152 conquer_1.0.2
[46] iterators_1.0.13 crosstalk_1.1.1 xfun_0.22
[49] rvest_1.0.0 mime_0.10 miniUI_0.1.1.1
[52] lifecycle_1.0.0 ECOSolveR_0.5.4 statmod_1.4.35
[55] nadiv_2.17.1 scales_1.1.1 hms_1.0.0
[58] promises_1.2.0.1 SparseM_1.81 yaml_2.2.1
[61] HaploSim_1.8.4 memoise_2.0.0 sass_0.3.1
[64] stringi_1.5.3 foreach_1.5.1 boot_1.3-27
[67] manipulateWidget_0.10.1 matrixStats_0.58.0 rlang_0.4.10
[70] pkgconfig_2.0.3 rgl_0.106.6 evaluate_0.14
[73] htmlwidgets_1.5.3 tidyselect_1.1.0 plyr_1.8.6
[76] generics_0.1.0 DBI_1.1.1 pillar_1.6.0
[79] haven_2.4.0 whisker_0.4 withr_2.4.2
[82] abind_1.4-5 scatterplot3d_0.3-41 cccp_0.2-7
[85] pspline_1.0-18 modelr_0.1.8 utf8_1.2.1
[88] alabama_2015.3-1 rmarkdown_2.7 grid_4.0.3
[91] readxl_1.3.1 minpack.lm_1.2-1 data.table_1.14.0
[94] git2r_0.28.0 reprex_2.0.0 digest_0.6.27
[97] webshot_0.5.2 xtable_1.8-4 httpuv_1.5.5
[100] numDeriv_2016.8-1.1 munsell_0.5.0 bslib_0.2.4
[103] magic_1.5-9 quadprog_1.5-8