Complementary functions that may help with handling parameters and routine operations.
Alencar Xavier
emGWA(y,gen) # Simple MLM for association analysis
markov(gen,chr=NULL) # Markovian imputation of genotypes coded as 012
IMP(X) # Imputes genotypes with SNP expectation (column average)
CNT(X) # Recodes SNPs by centralizing columns in a matrix
GAU(X) # Creates Gaussian kernel as exp(-Dist2/mean(Dist2))
GRM(X,Code012=FALSE) # Creates additive kinship matrix VanRaden 2008
SPC(y,blk,row,col,rN=3,cN=1) # Spatial covariate
SPM(blk,row,col,rN=3,cN=1) # Spatial design matrix
SibZ(id,p1,p2) # Pedigree design matrix compatible to regression methods
Hmat(ped,gen=NULL) # Kinship combining pedigree and genomics
EigenGRM(X, centralizeZ = TRUE, cores = 1) # GRM using Eigen library
EigenARC(X, centralizeZ = TRUE, cores = 1) # ArcCosine kernel
EigenGAU(X, phi = 1.0, cores = 1) # Gaussian kernel using Eigen library
EigenCNT(X, cores = 1) # Center SNPs without missing Eigen library
EigenEVD(A, cores = 1) # Eigendecomposition from Eigen library
EigenBDCSVD(X, cores = 1) # BDC single value composition from Eigen
EigenJacobiSVD(X, cores = 1) # Jacobi single value composition from Eigen
EigenAcc(X1, X2, h2 = 0.5, cores = 1) # Deterministic accuracy X1 -> X2 via V
AccByC(X1, X2, h2 = 0.5, cores = 1) # Deterministic accuracy X1 -> X2 via C
EigenArcZ(Zfndr, Zsamp, cores = 1) # Reduced rank ArcCos kernel PCs with founder rotation
EigenGauZ(Zfndr, Zsamp, phi=1, cores = 1) # Reduced rank Gaussian kernel PCs with founder rotation
K2X(K, MinEV = 1e-8, cores = 1) # Reparametrize kernel to PCs to run regression models
SimY(Z,k=5,h2=0.5,GC=0.5,seed=123,unbalanced=FALSE,PercMiss=0,BlkMiss=FALSE) # Simulate phenotypes
SimZ(ind=500,snp=500,chr=2,F2=TRUE,rec=0.01) # Simulate genome
SimGC(k=50,...) # Simulate genetic correlation matrix
MvSimY(Ufndr,Zfndr,Zsamp,GxY,GxL,H2plot,nLoc=20,Seed=123) # Simulate phenotypes given founders