mvb.simu {MVB} | R Documentation |
for given coefficients and design matrix, generate the corresponding responses according multivariate Bernoulli model
mvb.simu(coefficients, x, K = 2, offset = as.double(0))
coefficients |
coefficients matrix, number of columns should be
less than |
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. |
The response variables are simulated according to cononical link function of multivariate Bernoulli model with coefficients speicified.
response |
matrix for outcomes, with dimension |
beta |
expanded coefficients from input argument
|
mvbfit
, mvblps
# 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)