Nothing
ml.nbc <- function(formula, data, start = NULL, verbose = FALSE) {
mf <- model.frame(formula, data)
mt <- attr(mf, "terms")
y <- model.response(mf, "numeric")
nbcX <- model.matrix(formula, data = data)
nbc.reg.ml <- function(b.hat, X, y) {
a.hat <- b.hat[1]
xb.hat <- X %*% b.hat[-1]
mu.hat <- 1 / ((exp(-xb.hat)-1)*a.hat)
p.hat <- 1 / (1 + a.hat*mu.hat)
r.hat <- 1 / a.hat
sum(dnbinom(y,
size = r.hat,
prob = p.hat,
log = TRUE))
}
if (is.null(start))
start <- c(0.5, -1, rep(0, ncol(nbcX) - 1))
fit <- optim(start,
nbc.reg.ml,
X = nbcX,
y = y,
control = list(
fnscale = -1,
maxit = 10000),
hessian = TRUE
)
if (verbose | fit$convergence > 0) print(fit)
beta.hat <- fit$par
se.beta.hat <- sqrt(diag(solve(-fit$hessian)))
results <- data.frame(Estimate = beta.hat,
SE = se.beta.hat,
Z = beta.hat / se.beta.hat,
LCL = beta.hat - 1.96 * se.beta.hat,
UCL = beta.hat + 1.96 * se.beta.hat)
rownames(results) <- c("alpha", colnames(nbcX))
results <- results[c(2:nrow(results), 1),]
return(results)
}
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