loglike {MVB} | R Documentation |
evaluate negative loglikelihood of the corresponding family of model.
loglike(x, y, input, family = c("gaussian", "bernoulli", "mvbernoulli"))
x |
design matrix. |
y |
output binary matrix with number of columns equal to the number of outcomes per observation. |
input |
vector of the fitted coefficients for the distribution family. |
family |
a GLM family, currently support gaussian, binomial and mvbernoulli (multivariate Bernoulli). |
evaluate the negative log-likelihood to examine the performance of the model.
a double value returned as the negative log-likelihood
unifit
, mvbfit
# 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$response, output = 1) loglike(x, res$response, fitMVB$beta, "mvbernoulli")