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

Details

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

Value

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)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(MVB)
Loading required package: Rcpp
Loading required package: RcppArmadillo
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MVB/mvb.simu.Rd_%03d_medium.png", width=480, height=480)
> ### Name: mvb.simu
> ### Title: generate multivariate Bernoulli simulated data
> ### Aliases: 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)
fit started
iteration 0 gpnorm = 2.55056
iteration 8 gpnorm = 6.43026e-07
*** Converged ***
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>