confint {stats}

Confidence Intervals for Model Parameters

Description

Computes confidence intervals for one or more parameters in a fitted model. There is a default and a method for objects inheriting from class "lm".

Usage

confint(object, parm, level = 0.95, ...)
## Default S3 method:
confint(object, parm, level = 0.95, ...)
## S3 method for class 'lm'
confint(object, parm, level = 0.95, ...)
## S3 method for class 'glm'
confint(object, parm, level = 0.95, trace = FALSE, test=c("LRT", "Rao"), ...)
## S3 method for class 'nls'
confint(object, parm, level = 0.95, ...)

Arguments

object

a fitted model object.

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?

test

use Likelihood Ratio or Rao Score test in profiling.

...

additional argument(s) for methods.

Details

confint is a generic function. The default method assumes normality, and needs suitable coef and 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.

Methods for classes "glm" and "nls" call the appropriate profile method, then find the confidence intervals by interpolation in the profile traces. If the profile object is already available it can 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

Original versions: confint.glm and confint.nls in package MASS.

Examples

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) 
confint.default(glm.D93)  # based on asymptotic normality