Description Usage Arguments Details Value References See Also Examples
Computes confidence intervals for one or more parameters in a fitted
model. Package MASS adds methods for glm
and nls
fits.
1 2 3 4 5 
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32  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")

Waiting for profiling to be done...
Waiting for profiling to be done...
Waiting for profiling to be done...
d0 low high
110 kg: 261.5132 256.2303 267.5009
100 kg: 349.4979 334.7293 368.0151
90 kg: 507.0941 457.2637 594.8745
Waiting for profiling to be done...
2.5 % 97.5 %
sexF 4.4581438 2.613610
sexM 3.1728745 1.655117
ldose 0.8228708 1.339058
Waiting for profiling to be done...
2.5 % 97.5 %
0.8228708 1.3390581
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