confintMASS  R Documentation 
Computes confidence intervals for one or more parameters in a fitted
model. Package MASS adds methods for glm
and nls
fits.
## S3 method for class 'glm'
confint(object, parm, level = 0.95, trace = FALSE, ...)
## S3 method for class 'nls'
confint(object, parm, level = 0.95, ...)
object 
a fitted model object. Methods currently exist for the 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. 
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.
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%).
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
confint
(the generic and "lm"
method),
profile
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")
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