gam.check                package:mgcv                R Documentation

_S_o_m_e _d_i_a_g_n_o_s_t_i_c_s _f_o_r _a _f_i_t_t_e_d _g_a_m _m_o_d_e_l

_D_e_s_c_r_i_p_t_i_o_n:

     Takes a fitted 'gam' object produced by 'gam()' and produces some
     diagnostic information about the fitting procedure and results.
     The default is to produce 4 residual plots, and some information
     about the convergence of the smoothness selection optimization.

_U_s_a_g_e:

     gam.check(b)

_A_r_g_u_m_e_n_t_s:

       b: a fitted 'gam' object as produced by 'gam()'.

_D_e_t_a_i_l_s:

     This function plots 4 standard diagnostic plots, and some other
     convergence diagnostics. Usually the 4 plots are various residual
     plots. The printed information relates to the optimization used to
     select smoothing parameters. For the default optimization methods
     the information is summarized in a readable way, but for other
     optimization methods, whatever is returned by way of convergence
     diagnostics is simply printed.

_A_u_t_h_o_r(_s):

     Simon N. Wood simon.wood@r-project.org

_R_e_f_e_r_e_n_c_e_s:

     Wood S.N. (2006) Generalized Additive Models: An Introduction with
     R. Chapman and Hall/CRC Press.

     <URL: http://www.maths.bath.ac.uk/~sw283/>

_S_e_e _A_l_s_o:

     'choose.k',  'gam', 'mgcv', 'magic'

_E_x_a_m_p_l_e_s:

     library(mgcv)
     set.seed(0)
     dat <- gamSim(1,n=200)
     b<-gam(y~s(x0)+s(x1)+s(x2)+s(x3),data=dat)
     plot(b,pages=1)
     gam.check(b)