| intervals.lme {nlme} | R Documentation |
Confidence Intervals on lme Parameters
Description
Approximate confidence intervals for the parameters in the linear
mixed-effects model represented by object are obtained, using
a normal approximation to the distribution of the (restricted)
maximum likelihood estimators (the estimators are assumed to have a
normal distribution centered at the true parameter values and with
covariance matrix equal to the negative inverse Hessian matrix of the
(restricted) log-likelihood evaluated at the estimated parameters).
Confidence intervals are obtained in an unconstrained scale first,
using the normal approximation, and, if necessary, transformed to the
constrained scale. The pdNatural parametrization is used for
general positive-definite matrices.
Usage
## S3 method for class 'lme'
intervals(object, level = 0.95,
which = c("all", "var-cov", "fixed"), ...)
Arguments
object |
an object inheriting from class |
level |
an optional numeric value with the confidence level for the intervals. Defaults to 0.95. |
which |
an optional character string specifying the subset
of parameters for which to construct the confidence
intervals. Possible values are |
... |
some methods for this generic require additional arguments. None are used in this method. |
Value
a list with components given by data frames with rows corresponding to
parameters and columns lower, est., and upper
representing respectively lower confidence limits, the estimated
values, and upper confidence limits for the parameters. Possible
components are:
fixed |
fixed effects, only present when |
reStruct |
random effects variance-covariance parameters, only
present when |
corStruct |
within-group correlation parameters, only
present when |
varFunc |
within-group variance function parameters, only
present when |
sigma |
within-group standard deviation. |
Author(s)
José Pinheiro and Douglas Bates bates@stat.wisc.edu
References
Pinheiro, J.C., and Bates, D.M. (2000) "Mixed-Effects Models in S and S-PLUS", Springer.
See Also
lme, intervals,
print.intervals.lme,
pdNatural
Examples
fm1 <- lme(distance ~ age, Orthodont, random = ~ age | Subject)
intervals(fm1)