| intervals.gls {nlme} | R Documentation |
Approximate confidence intervals for the parameters in the linear
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.
## S3 method for class 'gls': intervals(object, level, which, ...)
object |
an object inheriting from class gls, representing
a generalized least squares fitted linear model. |
level |
an optional numeric value for the interval confidence level. Defaults to 0.95. |
which |
an optional character string specifying the subset
of parameters for which to construct the confidence
intervals. Possible values are "all" for all parameters,
"var-cov" for the variance-covariance parameters only, and
"coef" for the linear model coefficients only. Defaults to
"all". |
... |
some methods for this generic require additional arguments. None are used in this method. |
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:
coef |
linear model coefficients, only present when which
is not equal to "var-cov". |
corStruct |
correlation parameters, only present when
which is not equal to "coef" and a
correlation structure is used in object. |
varFunc |
variance function parameters, only present when
which is not equal to "coef" and a variance function
structure is used in object. |
sigma |
residual standard error. |
Jose Pinheiro Jose.Pinheiro@pharma.novartis.com and Douglas Bates bates@stat.wisc.edu
data(Ovary)
fm1 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary,
correlation = corAR1(form = ~ 1 | Mare))
intervals(fm1)