"ah4" Objectsprint and summary methods there are also some standard
extraction methods defined for objects of class "ah4" resulting
from a call to hhh4.## S3 method for class 'ah4':
print(x, digits = max(3, getOption("digits") - 3),
reparamPsi = TRUE, ...)
## S3 method for class 'ah4':
summary(object, ...)## S3 method for class 'ah4':
coef(object, se = FALSE, reparamPsi = TRUE,
idx2Exp = NULL, amplitudeShift = FALSE, ...)
## S3 method for class 'ah4':
fixef(object, ...)
## S3 method for class 'ah4':
ranef(object, tomatrix = FALSE, ...)
## S3 method for class 'ah4':
AIC(object, ..., k = 2)
## S3 method for class 'ah4':
logLik(object, ...)
## S3 method for class 'ah4':
confint(object, parm, level = 0.95,
reparamPsi = TRUE, idx2Exp = NULL, amplitudeShift = FALSE, ...)
"ah4".TRUE, the overdispersion parameter from the
negative binomial distribution is transformed from internal (log-)scale
to a user scale (where zero corresponds to a Poisson distribution).summary, fixef and ranef methods:
arguments passed to coef.
For the remaining methods: unused (argument of the generic).addSeason2formula) should be transformed
to an amplitude/shift formulation.FALSE (default), the vector of
all random effects is returned (as used internally). However, for
random intercepts of type="car", the number of parameters is
one less than the number of regions and the indk = 2 is the classical AIC.coef returns all estimated (regression)
parameters from a model as propsed by Paul and Held (2011), see
hhh4.
If the model includes random effects, those can be extracted with
ranef, whereas fixef returns the fixed
parameters. The function AIC returns the value of the AIC criterion
only for models without random effects, and returns NULL in
the case of a random effects model where AIC is problematic.
The function logLik returns an object of class "logLik"
in the case of a model without random effects, and the value of the
penalized log-likelihood at the parameter estimates otherwise.
The function confint returns Wald-type confidence intervals
(assuming asymptotic normality)
for one or more parameters in the fitted model.