MVB (version 1.1)

mvbme: multivariate Bernoulli mixed-effects model fitting

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.

Value

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

Details

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

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)