Confidence Intervals on gls Parameters

DESCRIPTION:

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, unless the control parameter natUnconstrained is set to FALSE (see the documentation on glsControl).

USAGE:

intervals(object, level, which) 

REQUIRED ARGUMENTS:

object
an object inheriting from class gls, representing a generalized least squares fitted linear model.

OPTIONAL ARGUMENTS:

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 "all" for all parameters, "var-cov" for the variance-covariance parameters only, and "coef" for the linear model coefficients only. Defaults to "all".

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:
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.

SEE ALSO:

,

EXAMPLES:

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