confint {stats} | R Documentation |
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"
.
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, ...)
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. |
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.
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.
Original versions: confint.glm
and
confint.nls
in package MASS.
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