minque: MINQUE estimation of variance components

In vcpen: Penalized Variance Components Analysis

Description

Estimate variance components by MINQUE method, allowing multiple iterations

Usage

minque(y, X, Kerns, n.iter = 1, eps = 0.001)

Arguments

y	Numeric vector of traits. Only continuous trait currently allowed.
X	Matrix of covariates (columns) for subjects (rows), matching subjects in the trait (y) vector.
Kerns	List of kernel matrices: a kernel matrix for each variance compenent. The last kernel matrix in the list (an identity matrix) is for the residual variance component.
n.iter	Number of minque iterations
eps	Default small positive value for non-positive vc estimates within iterations.

Value

List with estimates of variance components (vc), covariate regression coefficients (beta), and residuals of model fit.

Author(s)

JP Sinnwell, DJ Schaid

Examples

data(vcexample)
nvc <- 1+length(unique(doseinfo[,2]))
id <- 1:nrow(dose)
## vcs for genetic kernel matrices
Kerns <- vector("list", length=nvc)
for(i in 1:(nvc-1)){
  Kerns[[i]] <- kernel_linear(dose[,grep(i, doseinfo[,2])])
  rownames(Kerns[[i]]) <- id
  colnames(Kerns[[i]]) <- id
}
## vc for residual variance
Kerns[[nvc]] <- diag(nrow(dose))
rownames(Kerns[[nvc]]) <- id
colnames(Kerns[[nvc]]) <- id
prefit  <- minque(response, covmat, Kerns, n.iter=2)
prefit[1]
prefit[2]
fit <- vcpen(response, covmat, Kerns, vc_init = prefit$vc)

vcpen documentation built on Jan. 3, 2022, 5:12 p.m.