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
fm1 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary, correlation = corAR1(form = ~ 1 | Mare)) intervals(fm1)