Produces an ANODEV table for a set of GAM models, or else a summary for a single GAM model
# S3 method for Gam
anova(object, ..., test = c("Chisq", "F", "Cp"))# S3 method for Gam
summary(object, dispersion = NULL, ...)
a fitted Gam
other fitted Gams for anova
a character string specifying the test statistic to be used. Can be one of '"F"', '"Chisq"' or '"Cp"', with partial matching allowed, or 'NULL' for no test.
a dispersion parameter to be used in computing standard errors
Written by Trevor Hastie, following closely the design in the "Generalized Additive Models" chapter (Hastie, 1992) in Chambers and Hastie (1992).
These are methods for the functions anova or summary for
objects inheriting from class Gam. See anova for the general
behavior of this function and for the interpretation of test.
When called with a single Gam object, a special pair of anova tables for
Gam models is returned. This gives a breakdown of the degrees of freedom
for all the terms in the model, separating the projection part and
nonparametric part of each, and returned as a list of two anova objects. For
example, a term specified by s() is broken down into a single degree of
freedom for its linear component, and the remainder for the nonparametric
component. In addition, a type of score test is performed for each of the
nonparametric terms. The nonparametric component is set to zero, and the
linear part is updated, holding the other nonparametric terms fixed. This is
done efficiently and simulataneously for all terms.
Hastie, T. J. (1992) Generalized additive models. Chapter 7 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
Hastie, T. and Tibshirani, R. (1990) Generalized Additive Models. London: Chapman and Hall.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. New York: Springer.
data(gam.data)
Gam.object <- gam(y~s(x,6)+z,data=gam.data)
anova(Gam.object)
Gam.object2 <- update(Gam.object, ~.-z)
anova(Gam.object, Gam.object2, test="Chisq")
Run the code above in your browser using DataLab