R: Complete Data Log Density for Bernoulli-Normal Random Effects...

bernor {bernor}

R Documentation

Complete Data Log Density for Bernoulli-Normal Random Effects Model

Description

Evaluate the complete data log density for Bernoulli regression model with normal random effects.

Usage

bernor(y, beta, b, sigma, x, z, i, deriv = 0)

Arguments

`y`	a zero-one-valued (Bernoulli) vector, the response.
`beta`	the fixed effect vector.
`b`	the random effect vector.
`sigma`	the scale parameter vector for the fixed effects.
`x`	the model matrix for fixed effects.
`z`	the model matrix for random effects.
`i`	the index vector for random effects.
`deriv`	the number of derivatives wanted. No more than 2. Zero, the default, means no derivatives.

Details

evaluates the function given by the R statements

    eta <- x %*% beta + z %*% (sigma[i] * b)
    p <- 1 / (1 + exp(- eta))
    sum(dbinom(y, 1, p, log = TRUE)) + sum(dnorm(b, log = TRUE))

Value

A list containing some of the following components:

`value`	the function value.
`gradient`	the gradient vector. The length is `nparm`, which is `length(beta) + length(mu)`.
`hessian`	the hessian matrix. The dimension is `nparm` by `nparm`.

Examples

data(salam)
attach(salam)
beta <- rnorm(ncol(x))
sigma <- rgamma(length(unique(i)), 5, 5)
b <- rnorm(ncol(z))
bernor(y[ , 1], beta, b, sigma, x, z, i)
bernor(y[ , 1], beta, b, sigma, x, z, i, deriv = 2)

[Package bernor version 0.3-8 Index]