loglike: negative loglikelihood evaluation

In MVB: Mutivariate Bernoulli log-linear model

Description

evaluate negative loglikelihood of the corresponding family of model.

Usage

loglike(x, y, input,
        family = c("gaussian", "bernoulli", "mvbernoulli")) 

Arguments

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).

Details

evaluate the negative log-likelihood to examine the performance of the model.

Value

a double value returned as the negative log-likelihood

See Also

unifit, mvbfit

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$response, output = 1)
loglike(x, res$response, fitMVB$beta, "mvbernoulli")

Example output

Loading required package: Rcpp
Loading required package: RcppArmadillo
fit started
iteration 0 gpnorm = 2.5531
iteration 8 gpnorm = 9.6834e-07
*** Converged ***
[1] 0.2748443

MVB documentation built on May 2, 2019, 3:06 a.m.