MASS (version 7.3-58.3)

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.


# S3 method for glm
confint(object, parm, level = 0.95, trace = FALSE, ...)

# S3 method for nls confint(object, parm, level = 0.95, ...)


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%).



a fitted model object. Methods currently exist for the classes "glm", "nls" and for profile objects from these classes.


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.


the confidence level required.


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.


Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

confint (the generic and "lm" method), profile


Run this code
expn1 <- deriv(y ~ b0 + b1 * 2^(-x/th), c("b0", "b1", "th"),
               function(b0, b1, th, x) {}) <- 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(, w0))
    out <- rbind(out, c(coef(fm)["d0"], confint(fm, "d0")))
dimnames(out) <- list(paste(w0s, "kg:"),  c("d0", "low", "high"))

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, "ldose")

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