lm
,
glm
and
nls
fits.
confint(object, parm = <<see below>>, level = 0.95, ...)
"glm"
,
"nls"
and for profile objects from these classes.
glm
methods allows
trace
.
confint
calls the appropriate profile
method, then finds the confidence intervals by interpolation in the
profile traces. If the profile object is already available it should
be used as the main argument rather than the fitted model object
itself. For objects of class
"lm"
the
direct formulae based on t values are used.
expn1 <- deriv(y ~ b0 + b1 * 2^(-x/th), c("b0", "b1", "th"), function(b0, b1, th, x) {}) wtloss.gr <- nls(Weight ~ expn1(b0, b1, th, Days), data = wtloss, start = c(b0=90, b1=95, th=120)) expn2 <- deriv(~b0 + b1*((w0 - b0)/b1)^(x/d0), c("b0","b1","d0"), function(b0, b1, d0, x, w0) {}) wtloss.init <- function(obj, w0) { p <- coef(obj) d0 <- - log((w0 - p["b0"])/p["b1"])/log(2) * p["th"] c(p[c("b0", "b1")], d0 = as.vector(d0)) } out <- NULL w0s <- c(110, 100, 90) for(w0 in w0s) { fm <- nls(Weight ~ expn2(b0, b1, d0, Days, w0), wtloss, start = wtloss.init(wtloss.gr, w0)) out <- rbind(out, c(coef(fm)["d0"], confint(fm, "d0"))) } dimnames(out) <- list(paste(w0s, "kg:"), c("d0", "low", "high")) out ldose <- rep(0:5, 2) numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16) sex <- factor(rep(c("M", "F"), c(6, 6))) SF <- cbind(numdead, numalive = 20 - numdead) budworm.lg0 <- glm(SF ~ sex + ldose - 1, family = binomial) confint(budworm.lg0)