MVB (version 1.1)

mvb.simu: generate multivariate Bernoulli simulated data

Description

for given coefficients and design matrix, generate the corresponding responses according multivariate Bernoulli model

Usage

mvb.simu(coefficients, x, K = 2, offset = as.double(0))

Arguments

coefficients

coefficients matrix, number of columns should be less than 2^K.

x

design matrix.

K

number of outcomes for the model.

offset

non-penalized terms in coefficients, corresponding to a unit column in design matrix, which is generated automaticly.

Value

response

matrix for outcomes, with dimension nobs times K.

beta

expanded coefficients from input argument coefficients and offset.

Details

The response variables are simulated according to cononical link function of multivariate Bernoulli model with coefficients speicified.

See Also

mvbfit, mvblps

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)