mvb.simu: generate multivariate Bernoulli simulated data In MVB: Mutivariate Bernoulli log-linear model

Description

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

Usage

 `1` ```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.

Details

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

Value

 `response` matrix for outcomes, with dimension `nobs` times `K`. `beta` expanded coefficients from input argument `coefficients` and `offset`.

`mvbfit`, `mvblps`

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```# 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) ```

Example output

```Loading required package: Rcpp