Approximate confidence intervals for the parameters in the generalized
linear mixed-effects model represented
by
object are obtained,
using a normal approximation to the distribution of the (restricted)
PQL or MQL 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
lmeControl).
USAGE:
intervals(object, level, which)
REQUIRED ARGUMENTS:
object
an object inheriting from class
glme,
representing a fitted generalized linear mixed-effects model.
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
"fixed" for the fixed effects 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:
fixed
fixed effects, only present when the argument
which is not
equal to
"var-cov".
reStruct
random effects variance-covariance parameters,
only present when the argument
which is not equal
to
"fixed".
corStruct
within-group correlation parameters,
only present when the argument
which is not equal
to
"fixed"
and a correlation structure is used in
object.
varFunc
within-group variance function parameters,
only present when the argument
which is not equal
to
"fixed"
and a variance function structure is used
in
object.