Nothing
rm(list=ls())
load("c://source/medpar.RData")
medpar$type <- factor(medpar$type)
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))
}
# Create the design matrix
nbcX <- model.matrix(~ hmo + white + type, data = medpar)
# Starting points (discovered by trial and error!)
p.0 <- c(alpha = 0.5,
cons = -1,
hmo = 0,
white = 0,
type2 = 0,
type3 = 0)
# Maximize the joint conditional LL
nbc.fit <- optim(p.0,
nbc.reg.ml,
X = nbcX,
y = medpar$los,
control = list(
fnscale = -1,
maxit = 10000),
hessian = TRUE
)
# and obtain the parameter estimates and asymptotic SE's by
(nbc.beta.hat <- nbc.fit$par)
(nbc.se.beta.hat <- sqrt(diag(solve(-nbc.fit$hessian))))
nbc.results <- data.frame(Estimate = nbc.beta.hat,
SE = nbc.se.beta.hat,
Z = nbc.beta.hat / nbc.se.beta.hat,
LCL = nbc.beta.hat - 1.96 * nbc.se.beta.hat,
UCL = nbc.beta.hat + 1.96 * nbc.se.beta.hat)
rownames(nbc.results) <- c("alpha", colnames(nbcX))
nbc.results <- nbc.results[c(2:nrow(nbc.results), 1),]
nbc.results
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