Terms
and
L
are NULL
), a combination of model terms (when
Terms
in not NULL
), or linear combinations of the model
coefficients (when L
is not NULL
). Otherwise, when
multiple fitted objects are being compared, a data frame with
the degrees of freedom, the (restricted) log-likelihood, the
Akaike Information Criterion (AIC), and the Bayesian Information
Criterion (BIC) of each object is returned. If test=TRUE
,
whenever two consecutive objects have different number of degrees of
freedom, a likelihood ratio statistic, with the associated p-value is
included in the returned data frame.## S3 method for class 'gls':
anova(object, \dots, test, type, adjustSigma, Terms, L, verbose)
gls
,
representing a generalized least squares fit.gls
, gnls
, lm
, lme
,
lmList
, nlme
, nlsList
, or nls
.object
and the objects in ...
. Defaults to
TRUE
."sequential"
, the sequential sum of squares obtained by
including the terms in the order they appear in the modeTRUE
and the
estimation method used to obtain object
was maximum
likelihood, the residual standard error is multiplied by
$\sqrt{n_{obs}/(n_{obs} - n_{par})}$,
converting it to a REML-likTRUE
, the calling
sequences for each fitted model object are printed with the rest of
the output, being omitted if verbose = FALSE
. Defaults to
FALSE
.anova.lme
.gls
, gnls
, lme
,
AIC
, BIC
, print.anova.lme
# AR(1) errors within each Mare
fm1 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary,
correlation = corAR1(form = ~ 1 | Mare))
anova(fm1)
# variance changes with a power of the absolute fitted values?
fm2 <- update(fm1, weights = varPower())
anova(fm1, fm2)
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