This includes basic mixed models with fixed and uncorrelated random effects, random regression models for genotypes (rrBLUP) or covariates, and mixed model with pedigree.

The call is:

estmm <- bayz.mm(data=d, resp=y, fixmod=f, ranmod=r, chain=c(t,b,s))

The call for using genotypes is:

estmmg <- bayz.mmg(data=and for using covariates is:d, geno=g, resp=y, fixmod=f, ranmod=r, chain=c(t,b,s))

estmmc <- bayz.mmc(data=The genotypes or covariates in the geno= or covar= table are treated with random regressions (apart from storage and details on missing values the two versions are the same). Additional random effects can still be added using the ranmod= setting. This model is known as the rrBLUP model.d, covar=c, resp=y, fixmod=f, ranmod=r, chain=c(t,b,s))

The call is:

estmmp <- bayz.mmp(data=Additional uncorrelated random effects can still be added using the ranmod= setting.d, ped=p, resp=y, fixmod=f, ranmod=r, chain=c(t,b,s))

There is also a version for replicated phenotypic data, bayz.mmpr, and the arguments are the same. Note: the model for replicated phenotypes fits an additional random effect to allow for additional correlation between the replicated records within ID (Permanent Environment effect for animal data). You should not add an extra ID effect to account for this, as bayz already does it automatically.

There is a version using genotypes (bayz.bplg), or using covariates (bayz.bplc), and a version for repeated phenotypes using covariates per ID (bayz.bplcr).

The call for bayz.bplg is:

estbplg <- bayz.bplg(data=The call is the same as bayz.mmg, except for an added pow= setting, which sets the power parameter for the Power LASSO model. The default is pow=0.5, a setting pow=1 makes the standard LASSO, pow=2 makes the rrBLUP model (but less efficient). Usually, pow is used in the range 0.3-0.7 to make a distribution which is more peaked and has longer tails than the standard LASSO.d, geno=g, resp=y, fixmod=f, ranmod=r, pow=0.5, chain=c(t,b,s))

The call for bayz.bplc replaces geno= with covar= table, similar as difference between bayz.mmg and bayz.mmc.

The call for bayz.bplcr is the same is bayz.bplc but allows replicated phenotypes. It fits an additional ID variance (see bayz.mmp comments).

The bvs1 models are an implementation of BayesCpi, with a 2-class mixture model and estimated proportions (pi) for SNPs with small and large effects.
In the bayz version, variance between small and large SNP effects have a fixed ratio.

The call for bayz.bvs1g (version with genotypes) is:

estbvs1g <- bayz.bvs1g(data=The additional parameters for bvs1g are: piprior, which sets the Beta prior distribution to estimate the proportion (pi), it is usually varied between 10,1 to 1000,1 (with default 100,1), which gives prior information to push pi to have most SNPs in the small effect group. A uniform prior on pi is Beta 1,1. The varratio parameter sets the fixed ratio between large and small SNP effects, it usually varied from 10 to 1000, with default 100.d, geno=g, resp=y, fixmod=f, ranmod=r, piprior=c(100,1), varratio=100, chain=c(t,b,s))

There is also a version for covariate (bayz.bvs1c), and for repeated phenotypes and covariates (bayz.bvs1cr).