anova.gam                package:mgcv                R Documentation

Hypothesis tests related to  GAM fits

Description:

     Performs hypothesis tests relating to one or more fitted 'gam'
     objects. For a single fitted 'gam' object, Wald tests of the
     significance of each parametric and smooth term are performed.
     Otherwise the fitted models are compared using an analysis of
     deviance table. The tests are usually approximate, unless the
     models are un-penalized.

Usage:

     ## S3 method for class 'gam':
     anova(object, ..., dispersion = NULL, test = NULL)
     ## S3 method for class 'anova.gam':
     print(x, digits = max(3, getOption("digits") - 3),...)

Arguments:

object,...: fitted model objects of class 'gam' as produced by 'gam()'.

       x: an 'anova.gam' object produced by a single model call to
          'anova.gam()'.

dispersion: a value for the dispersion parameter: not normally used.

    test: what sort of test to perform for a multi-model call. One of
          '"Chisq"', '"F"' or '"Cp"'. 

  digits: number of digits to use when printing output.

Details:

     If more than one fitted model is provided than 'anova.glm' is
     used. If only one model is provided then the significance of each
     model term is assessed using Wald tests: see 'summary.gam' for
     details of the actual computations. In the latter case
     'print.anova.gam' is used as the printing method.  Note that the
     p-values for smooth terms are approximate only.

Value:

     In the multi-model case '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.

WARNING:

     P-values for smooth terms are only approximate.

Author(s):

     Simon N. Wood simon.wood@r-project.org with substantial
     improvements by Henric Nilsson.

See Also:

     'gam', 'predict.gam', 'gam.check', 'summary.gam'

Examples:

     library(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")

