runMtCrossVarPredsAD: Multi-trait, multi-cross prediction of additive _and_...
In wolfemd/predCrossVar: Prediction of family genomic variances and covariances

Description Usage Arguments

User specifies a trait variance (or trait-trait covariance) to predict. Wrapper around 'predCrossVarsAD()' for predicting all trait variances and trait-trait covariances across multiple families for both additive _and_ dominance components. Input a data.frame of crosses to predict. NOTE: Marker effects should represent allele substitution effects.

runMtCrossVarPredsAD(
  outprefix = NULL,
  outpath = NULL,
  predType = "VPM",
  CrossesToPredict,
  AddEffectList,
  DomEffectList,
  haploMat,
  recombFreqMat,
  ncores = 1,
  ...
)

`outprefix`	string, prefix for *.rds file to be written to disk with output. DEFAULT = NULL (no disk write)
`outpath`	string, path to disk location where files should be written. Can be left DEFAULT = NULL (no disk write)
`predType`	Default = "VPM". string, "VPM" or "PMV" Choose option "VPM" if you have REML marker effect estimates (or posterior-means from MCMC) one set of marker effect estimates per trait. Variance of posterior means is faster but the alternative predType=="PMV" is expected to be less biassed. PMV requires user to supply a (probably LARGE) variance-covariance matrix of effects estimates.
`CrossesToPredict`	data.frame or tibble, col/colnames: sireID, damID. sireID and damID must both be in the haploMat.
`AddEffectList`	List of ADDITIVE effect matrices. One matrix per trait. Each element of the list is named with a string identifying the trait and the colnames of each matrix are labelled with snpIDs. If users effects are from REML or posterior-means MCMC, matrices will be of dimension 1 x N SNP. If users chose predType="PMV", each matrix will be dimension N thinned-MCMC sample x N SNP.
`DomEffectList`	List of DOMINANCE effect matrices. One matrix per trait. Each element of the list is named with a string identifying the trait and the colnames of each matrix are labelled with snpIDs. If users effects are from REML or posterior-means MCMC, matrices will be of dimension 1 x N SNP. If users chose predType="PMV", each matrix will be dimension N thinned-MCMC sample x N SNP.
`haploMat`	matrix of phased haplotypes, 2 rows per sample, cols = loci, 0,1, rownames assumed to contain GIDs with a suffix, separated by "_" to distinguish haplotypes
`recombFreqMat`	a square symmetric matrix with values = (1-2*c1), where c1=matrix of expected recomb. frequencies. The choice to do 1-2c1 outside the function was made for computation efficiency; every operation on a big matrix takes time.
`ncores`	If ncores set > 1 parallelizes across families, but beware it is memory intensive and options(future.globals.maxSize=___) may need to be adjusted.
`...`