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# NB1 maximum likelihood function J Hilbe 11Apr 2010
ml.nb1 <- function(formula, data, start = NULL, verbose = FALSE) {
mf <- model.frame(formula, data)
mt <- attr(mf, "terms")
y <- model.response(mf, "numeric")
nb1X <- model.matrix(formula, data = data)
nb1.reg.ml <- function(b.hat, X, y) {
a.hat <- b.hat[1]
xb.hat <- X %*% b.hat[-1]
mu.hat <- exp(xb.hat)
r.hat <- (1/a.hat) * mu.hat
sum(dnbinom(y,
size = r.hat,
mu = mu.hat,
log = TRUE))
}
if (is.null(start))
start <- c(0.5, -1, rep(0, ncol(nb1X) - 1))
fit <- optim(start,
nb1.reg.ml,
X = nb1X,
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$hessian)))
results <- data.frame(Estimate = beta.hat,
SE = se.beta.hat,
Z = beta.hat / se.beta.hat,
LCL = beta.hat - 1.96 * se.beta.hat,
UCL = beta.hat + 1.96 * se.beta.hat)
rownames(results) <- c("alpha", colnames(nb1X))
results <- results[c(2:nrow(results), 1),]
return(results)
}
load("c://source/medpar.RData")
medpar$type <- factor(medpar$type)
med.nb1 <- ml.nb1(los ~ hmo + white + type, data = medpar)
med.nb1
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