confint-MASS
Confidence Intervals for Model Parameters
Computes confidence intervals for one or more parameters in a fitted
model. Package MASS adds methods for glm and nls fits.
- Keywords
- models
Usage
# S3 method for glm
confint(object, parm, level = 0.95, trace = FALSE, …)# S3 method for nls
confint(object, parm, level = 0.95, …)
Arguments
- object
a fitted model object. Methods currently exist for the classes
"glm","nls"and for profile objects from these classes.- parm
a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.
- level
the confidence level required.
- trace
logical. Should profiling be traced?
- …
additional argument(s) for methods.
Details
confint is a generic function in package stats.
These confint methods call the appropriate profile method,
then find 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.
Value
A matrix (or vector) with columns giving lower and upper confidence limits for each parameter. These will be labelled as (1 - level)/2 and 1 - (1 - level)/2 in % (by default 2.5% and 97.5%).
References
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
See Also
Examples
# NOT RUN {
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)
confint(budworm.lg0, "ldose")
# }