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.

     For 'mgcv' based fits (not the default), the first plot shows the
     GCV or UBRE score against model  degrees of freedom, given the
     final estimates of the relative smoothing parameters for the
     model. This is a slice through the  GCV/UBRE score function that
     passes through the minimum found during fitting. Although not
     conclusive (except in the single  smoothing parameter case), a
     lack of multiple local minima on this plot is suggestive of a lack
     of multiple local minima  in the GCV/UBRE function and is
     therefore a good thing. Multiple local minima on this plot
     indicates that the GCV/UBRE function  may have multiple local
     minima, but in a multiple smoothing parameter case this is not
     conclusive - multiple local minima on one slice  through a
     function do not necessarily imply that the function has multiple
     local minima. A `good' plot here is a smooth curve with  only one
     local minimum (which is therefore its global minimum).

     The location of the minimum used for the fitted model is also
     marked on the first plot. Sometimes this location may be a local
     minimum that is not the global minimum on the plot. There is a
     legitimate reason for this to happen, and it does not always
     indicate problems. Smoothing parameter selection is based on
     applying GCV/UBRE to the approximating linear model produced by
     the GLM IRLS fitting method employed in 'gam.fit()'. It is
     sometimes possible for these approximating models to develop
     `phantom' minima in their GCV/UBRE scores. These  minima usually
     imply a big change in model parameters, and have the
     characteristic that the minimia will not be present in the
     GCV/UBRE score  of the approximating model that would result from
     actually applying this parameter change. In other words, these are
     spurious minima in regions  of parameter space well beyond those
     for which the weighted least squares problem can be expected to
     represent the real underlying likelihood well. Such minima can
     lead to convergence problems. To help ensure convergence even in
     the presence of phantom minima,  'gam.fit' switches to a cautious
     optimization mode after a user controlled number of iterations of
     the IRLS algorithm (see gam.control). In the presence of local
     minima in the GCV/UBRE score, this method selects the minimum that
     leads to the smallest change in  model estimated degrees of
     freedom. This approach is usually sufficient to deal with phantom
     minima. Setting 'trace' to 'TRUE' in  'gam.control' will allow you
     to check exactly what is happening. 

     If the location of the point indicating the minimum is not on the
     curve showing the GCV/UBRE function then there are numerical
     problems with the  estimation of the effective degrees of freedom:
     this usually reflects problems with  the relative scaling of
     covariates that are arguments of a single smooth.  In this
     circumstance reported estimated degrees of freedom can not be
     trusted, although the fitted model and term estimates are likely
     to be quite  acceptable. 

     If the fit method is based on 'magic' or 'gam.fit2' or
     'gam.fit3'then there is no global search and the problems with
     phantom local minima are much reduced. These more recent methods
     are also much more robust than the 'mgcv' based methods.

_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)
     n<-200
     sig<-2
     x0 <- runif(n, 0, 1)
     x1 <- runif(n, 0, 1)
     x2 <- runif(n, 0, 1)
     x3 <- runif(n, 0, 1)
     y <- 2 * sin(pi * x0)
     y <- y + exp(2 * x1) - 3.75887
     y <- y+0.2*x2^11*(10*(1-x2))^6+10*(10*x2)^3*(1-x2)^10-1.396
     e <- rnorm(n, 0, sig)
     y <- y + e
     b<-gam(y~s(x0)+s(x1)+s(x2)+s(x3))
     plot(b,pages=1)
     gam.check(b)

