Nothing
"rad.preempt" <-
function (x, family = poisson, ...)
{
canfun <- function(p, x, ...) {
if (length(x) <= 1)
return(0)
p <- plogis(p)
if (p == 1)
p <- 1 - .Machine$double.eps
fv <- linkinv(logJ + log(p) + log(1 - p) * rnk)
n <- rep(1, length(fv))
dev <- sum(dev.resids(x, fv, wt))
aicfun(x, n, fv, wt, dev)/2
}
fam <- family(link = "log")
aicfun <- fam$aic
linkinv <- fam$linkinv
dev.resids <- fam$dev.resids
x <- as.rad(x)
nsp <- length(x)
rnk <- seq(along = x) - 1
wt <- rep(1, length(x))
logJ <- log(sum(x))
p <- qlogis(0.1)
canon <- try(nlm(canfun, p = p, x = x, rnk = rnk, logJ = logJ,
wt = wt, hessian = TRUE, ...))
if (inherits(canon, "try-error")) {
aic <- rdf <- deviance <- NA
p <- rep(NA, 1)
fit <- residuals <- wt <- rep(NA, length(x))
} else {
if (nsp > 1) {
p <- plogis(canon$estimate)
fit <- exp(logJ + log(p) + log(1 - p) * rnk)
} else {
p <- if (nsp > 0) 1 else NA
fit <- x
}
res <- dev.resids(x, fit, wt)
deviance <- sum(res)
residuals <- x - fit
if (nsp > 0)
aic <- aicfun(x, rep(1, length(x)), fit, wt, deviance) + 2
else
aic <- NA
rdf <- length(x) - 1
}
names(fit) <- names(x)
names(p) <- c("alpha")
out <- list(model = "Preemption", family = fam, y = x, coefficients = p,
fitted.values = fit, aic = aic, rank = 1, df.residual = rdf,
deviance = deviance, residuals = residuals, prior.weights = wt)
class(out) <- c("radline", "glm")
out
}
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