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.

_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. Output differs depending on whether the
     underlying fitting method was 'mgcv' or another method (see
     'gam.method').

     For 'mgcv' based fits , 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' then there is
     no global search and the problems with phantom local minima are
     much reduced. The first plot in this case will simply be a normal
     QQ plot of the standardized residuals.

     The other 3 plots are two residual plots and plot of fitted values
     against original data.

     The function also prints out information about the convergence of
     the GCV minimization algorithm, indicating how  many iterations 
     were required to minimise the GCV/UBRE score. A message is printed
     if the minimization terminated by  failing to improve the score
     with a steepest descent step: otherwise minimization terminated by
     meeting convergence criteria. The mean absolute gradient or RMS
     gradient of the GCV/UBRE function at the minimum is given. An
     indication of whether or not the Hessian of the GCV/UBRE function
     is positive definite is given. If some smoothing parameters  are
     not well defined (effectively zero, or infinite) then it may not
     be, although this is not usually a problem. If the fit method is
     'mgcv', a message is printed  if the second guess smoothing
     parameters did not improve on the first guess - this is primarily
     there for the developer.  For other fit methods the estimated rank
     of the model is printed.

     The ideal results from this function have a smooth, single minima
     GCV/UBRE plot (where appropriate),  good residual plots, and
     convergence to small  gradients with a positive definite Hessian.
     However, failure to meet some of these criteria  is often 
     acceptable,  and the information provided is primarily of use in
     diagnosing suspected problems.  High gradients at convergence  are
     a clear indication of problems, however.

     Fuller data can be extracted from 'mgcv.conv' part of the 'gam'
     object.

_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. (2000)  Modelling and Smoothing Parameter Estimation
     with Multiple  Quadratic Penalties. J.R.Statist.Soc.B
     62(2):413-428

     Wood, S.N. (2003) Thin plate regression splines. J.R.Statist.Soc.B
     65(1):95-114

     <URL: http://www.stats.gla.ac.uk/~simon/>

_S_e_e _A_l_s_o:

     '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)

