gam objects. For a single fitted gam object, Wald tests of
the significance of each parametric and smooth term are performed, so interpretation
is analogous to drop1 rather than anova.lm (i.e. it's like type III ANOVA,
rather than a sequential type I ANOVA). Otherwise
the fitted models are compared using an analysis of deviance table: this latter approach
should not be use to test the significance of terms which can be penalized
to zero. See details.## S3 method for class 'gam':
anova(object, ..., dispersion = NULL, test = NULL,
freq = FALSE,p.type=0)
## S3 method for class 'anova.gam':
print(x, digits = max(3, getOption("digits") - 3),...)gam as produced by gam().anova.gam object produced by a single model call to anova.gam()."Chisq", "F" or "Cp".summary.gam for details.summary.gam for details.anova.gam produces output identical to
anova.glm, which it in fact uses.In the single model case an object of class anova.gam is produced,
which is in fact an object returned from summary.gam.
print.anova.gam simply produces tabulated output.
Default P-values will usually be wrong for parametric terms penalized using `paraPen': use freq=TRUE to obtain better p-values when the penalties are full rank and represent conventional random effects.
For a single model, interpretation is similar to drop1, not anova.lm.
anova.glm is
used, with the difference in model degrees of freedom being taken as the difference
in effective degress of freedom. The p-values resulting from this are only approximate,
and must be used with care. The approximation is most accurate when the comparison
relates to unpenalized terms, or smoothers with a null space of dimension greater than zero.
(Basically we require that the difference terms could be well approximated by unpenalized
terms with degrees of freedom approximately the effective degrees of freedom). In simulations the
p-values are usually slightly too low. For terms with a zero-dimensional null space
(i.e. those which can be penalized to zero) the approximation is often very poor, and significance
can be greatly overstated: i.e. p-values are often substantially too low. This case applies to random effect terms. Note also that in the multi-model call to anova.gam, it is quite possible for a model with more terms to end up with lower effective degrees of freedom, but better fit, than the notionally null model with fewer terms. In such cases it is very rare that it makes sense to perform any sort of test, since there is then no basis on which to accept the notional null model.
If only one model is provided then the significance of each model term
is assessed using Wald like tests, conditional on the smoothing parameter estimates: see summary.gam
and Wood (2013a,b) for details. The p-values provided here are better justified than in the multi model case, and have close to the
correct distribution under the null, unless smoothing parameters are poorly identified. ML or REML smoothing parameter selection leads to
the best results in simulations as they tend to avoid occasional severe undersmoothing. In replication of the full simulation study of Scheipl et al. (2008) the tests give almost indistinguishable power to the method recommended there, but slightly too low p-values under the null in their section 3.1.8 test for a smooth interaction (the Scheipl et al. recommendation is not used directly, because it only applies in the Gaussian case, and requires model refits, but it is available in package RLRsim).
In the single model case print.anova.gam is used as the printing method.
By default the p-values for parametric model terms are also based on Wald tests using the Bayesian
covariance matrix for the coefficients. This is appropriate when there are "re" terms present, and is
otherwise rather similar to the results using the frequentist covariance matrix (freq=TRUE), since
the parametric terms themselves are usually unpenalized. Default P-values for parameteric terms that are
penalized using the paraPen argument will not be good.
Wood, S.N. (2013a) On p-values for smooth components of an extended generalized additive model. Biometrika 100:221-228
Wood, S.N. (2013b) A simple test for random effects in regression models. Biometrika 100:1005-1010
gam, predict.gam,
gam.check, summary.gamlibrary(mgcv)
set.seed(0)
dat <- gamSim(5,n=200,scale=2)
b<-gam(y ~ x0 + s(x1) + s(x2) + s(x3),data=dat)
anova(b)
b1<-gam(y ~ x0 + s(x1) + s(x2),data=dat)
anova(b,b1,test="F")Run the code above in your browser using DataLab