Confidence Intervals for Model Parameters
Computes confidence intervals for one or more parameters in a fitted
model. There is a default and a method for objects inheriting from class
confint(object, parm, level = 0.95, …)
a fitted model object.
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.
additional argument(s) for methods.
confint is a generic function. The default method assumes
asymptotic normality, and needs suitable
vcov methods to be available. The default method can be
called directly for comparison with other methods.
For objects of class
"lm" the direct formulae based on \(t\)
values are used.
There are stub methods in package stats for classes
"nls" which call those in package MASS (if
installed): if the MASS namespace has been loaded, its
methods will be used directly. (Those methods are based on profile
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%).
fit <- lm(100/mpg ~ disp + hp + wt + am, data = mtcars) confint(fit) confint(fit, "wt") ## from example(glm) counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3, 1, 9); treatment <- gl(3, 3) glm.D93 <- glm(counts ~ outcome + treatment, family = poisson()) confint(glm.D93) # needs MASS to be installed confint.default(glm.D93) # based on asymptotic normality