mvbme: multivariate Bernoulli mixed-effects model fitting

In MVB: Mutivariate Bernoulli log-linear model

Description

fit multivariate Bernoulli mixed-effects model using Laplacian approximation.

Usage

mvbme(x, y, z, maxOrder = 2,
       output = 0, printIter = 100)

Arguments

x	input design matrix.
y	output binary matrix with number of columns equal to the number of outcomes per observation.
z	random effect design matrix.
maxOrder	maximum order of interactions to be considered in outcomes.
output	with values 0 or 1, indicating whether the fitting process is muted or not.
printIter	Number of iterations to be printed if output is true.

Details

The mvbme utilize the class structure of the underlying C++ code and fitted the model with Laplacian approximation.

Value

An object of class mvbfit, for which some methods are available.

See Also

mvblps, unifit, stepfit, mvb.simu

Examples

# fit a simple MVB log-linear model
n <- 1000
p <- 5
kk <- 2
tt <- NULL
alter <- 1
for (i in 1:kk) {
  vec <- rep(0, p)
  vec[i] <- alter
  alter <- alter * (-1)
  tt <- cbind(tt, vec)
}
tt <- 1.5 * tt
tt <- cbind(tt, c(rep(0, p - 1), 1))

x <- matrix(rnorm(n * p, 0, 4), n, p)
res <- mvb.simu(tt, x, K = kk, rep(.5, 2))
fitMVB <- mvbfit(x, res$response, output = 1)

MVB documentation built on May 2, 2019, 3:06 a.m.