anova.gam                package:mgcv                R Documentation

_H_y_p_o_t_h_e_s_i_s _t_e_s_t_s _r_e_l_a_t_e_d _t_o  _G_A_M _f_i_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

     anova.gam(object, ..., dispersion = NULL, test = NULL)
     print.anova.gam(x, digits = max(3, getOption("digits") - 3),...)

_A_r_g_u_m_e_n_t_s:

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.

_D_e_t_a_i_l_s:

     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.

     P-values are usually reliable if the smoothing parameters are
     known, or the model is unpenalized. If smoothing parameters have
     been estimated then the p-values are typically somewhat too low
     under the null. This occurs because the uncertainty associated
     with the smoothing parameters is neglected in the calculations of
     the distributions under the null, which tends to lead to
     underdispersion in these distributions, and in turn to p-value
     estimates that are too low. (In simulations where the null is
     correct, I have seen p-values that are as low as half of what they
     should be.) Note however that tests can have low power if the
     estimated rank of the test statistic is much higher than the EDF,
     so that p-values can also be too low in some cases. 

     If it is important to have  p-values that are as accurate as
     possible, then, at least in the single model case, it is probably
     advisable to perform tests using unpenalized smooths (i.e.
     's(...,fx=TRUE)') with the basis dimension, 'k', left at what
     would have been used with penalization. Such tests are not as
     powerful, of course, but the p-values are more accurate under the
     null. Whether or not extra accuracy is required will usually
     depend on whether or not hypothesis testing is a key objective of
     the analysis.

_V_a_l_u_e:

     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.

_W_A_R_N_I_N_G:

     P-values are only approximate, particularly as a result of
     ignoring smoothing parameter uncertainty.

_A_u_t_h_o_r(_s):

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

_S_e_e _A_l_s_o:

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

_E_x_a_m_p_l_e_s:

     library(mgcv)
     set.seed(0)
     n<-200
     sig<-2
     x0 <- rep(1:4,50)
     x1 <- runif(n, 0, 1)
     x2 <- runif(n, 0, 1)
     x3 <- runif(n, 0, 1)
     y <- 2 * x0
     y <- y + exp(2 * x1)
     y <- y + 0.2 * x2^11 * (10 * (1 - x2))^6 + 10 * (10 * x2)^3 * (1 - x2)^10
     e <- rnorm(n, 0, sig)
     y <- y + e
     x0<-as.factor(x0)
     b<-gam(y~x0+s(x1)+s(x2)+s(x3))
     anova(b)
     b1<-gam(y~x0+s(x1)+s(x2))
     anova(b,b1,test="F")

