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. Simulation evidence  suggests that best
     p-value performance results from using ML estimated smoothing
     parameters.

_U_s_a_g_e:

     ## S3 method for class 'gam':
     anova(object, ..., dispersion = NULL, test = NULL,
                         alpha = 0, freq = FALSE)
     ## S3 method for class 'anova.gam':
     print(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"'. 

   alpha: adjustment to degrees of freedom per estimated smoothing
          parameter for a  term when called with a single model object.
          See 'summary.gam' for details.

    freq: whether to use frequentist or Bayesian approximations for
          single smooth term  p-values. See 'summary.gam' for details.

  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.  Note that the
     p-values for smooth terms are approximate only: simulation
     evidence suggests that they work best with REML or ML smoothness
     selection.

_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 for smooth terms are only approximate.

_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)
     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")