XTRA 1 {bWGR} | R Documentation |
Function to solve univariate mixed models with or without the usage of omic information. This function allows single-step modeling of replicated observations with marker information available through the usage of a linkage function to connect to a whole-genome regression method. Genomic estimated values can be optionally deregressed (no shrinkage) while fitting the model.
mixed(y,random=NULL,fixed=NULL,data=NULL,X=list(), alg=emML,maxit=10,Deregress=FALSE,...)
y |
Response variable from the data frame containg the dataset. |
random |
Formula. Right-hand side formula of random effects. |
fixed |
Formula. Right-hand side formula of fixed effects. |
data |
Data frame containing the response variable, random and fixed terms. |
X |
List of omic incidence matrix. Row names of these matrices connect the omic information to the levels of the indicated random terms (eg. |
alg |
Function. Whole-genome regression algorithm utilized to solve link functions. These include MCMC ( |
maxit |
Integer. Maximum number of iterations. |
Deregress |
Logical. Deregress (unshrink) coefficients while fitting the model? |
... |
Additional arguments to be passed to the whole-genome regression algorithms especified on |
The model for the whole-genome regression is as follows:
y = Xb + Zu + Wa + e
where y is the response variable, Xb corresponds to the fixed effect term, Zu corresponds to one or more random effect terms, W is the incidence matrix of terms with omic information and a is omic values by a=Mg, where M is the genotypic matrix and g are marker effects. Here, e is the residual term. An example is provided using the data from the NAM package with: demo(mixedmodel)
.
The function wgr returns a list with Fitness values (Fitness
) containing observation obs
, fitted values hat
, residuals res
, and fitted values by model term fits
; Estimated variance components (VarComp
) containing the variance components per se (VarComponents
) and variance explained by each model term (VarExplained
), regression coefficients by model term (Coefficients
), and the effects of structured terms (Structure
) containing the marker effects of each model term where markers were provided.
Alencar Xavier
## Not run: demo(mixedmodel) ## End(Not run)