summary.gam               package:mgcv               R Documentation

_S_u_m_m_a_r_y _f_o_r _a _G_A_M _f_i_t

_D_e_s_c_r_i_p_t_i_o_n:

     Takes a fitted 'gam' object produced by 'gam()' and produces
     various useful summaries from it. (See 'sink' to divert output to
     a file.)

_U_s_a_g_e:

     ## S3 method for class 'gam':
     summary(object, dispersion=NULL, freq=FALSE, ...)

     ## S3 method for class 'summary.gam':
     print(x,digits = max(3, getOption("digits") - 3), 
                       signif.stars = getOption("show.signif.stars"),...)

_A_r_g_u_m_e_n_t_s:

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

       x: a 'summary.gam' object produced by 'summary.gam()'.

dispersion: A known dispersion parameter. 'NULL' to use estimate or
          default (e.g. 1 for Poisson).

    freq: By default p-values for individual terms are calculated using
          the frequentist estimated covariance matrix of the parameter
          estimators. If this is set to FALSE then the Bayesian
          covariance matrix of the parameters is used instead. See
          details. 

  digits: controls number of digits printed in output.

signif.stars: Should significance stars be printed alongside output.

     ...: other arguments.

_D_e_t_a_i_l_s:

     Model degrees of freedom are taken as the trace of the influence
     (or hat) matrix A for the model fit. Residual degrees of freedom
     are taken as number of data minus model degrees of freedom.  Let
     P_i be the matrix  giving the parameters of the ith smooth when
     applied to the data (or pseudodata in the generalized case) and
     let X  be the design matrix of the model. Then tr(XP_i) is the edf
     for the ith term. Clearly this definition causes the edf's to add
     up properly! 

     'print.summary.gam' tries to print various bits of summary
     information useful for term selection in a pretty way.

     If 'freq=TRUE' then the frequentist approximation for p-values of
     smooth terms described in section 4.8.5 of Wood (2006) is used.
     The approximation is not great.  If p_i  is the parameter vector
     for the ith smooth term, and this term has estimated covariance
     matrix V_i then the  statistic is p_i'V_i^{k-}p_i, where V_i^{k-}
     is the rank k  pseudo-inverse of V_i, and k is estimated rank of  
     V_i. p-values are obtained as follows. In the case of known
     dispersion parameter, they are obtained by comparing the chi.sq
     statistic to the  chi-squared distribution with k degrees of
     freedom, where k is the estimated rank of  V_i. If the dispersion
     parameter is unknown (in  which case it will have been estimated)
     the statistic is compared to an F distribution with k upper d.f. 
     and lower d.f. given by the residual degrees of freedom for the
     model.  Typically the p-values will be somewhat too low.

     If 'freq=FALSE' then `Bayesian p-values' are returned for the
     smooth terms, based on a test statistic motivated by Nychka's
     (1988) analysis of the frequentist properties of Bayesian
     confidence intervals for smooths.  These appear to have better
     frequentist performance (in terms of power and distribution under
     the null)  than the alternative strictly frequentist
     approximation. Let f denote the vector of  values of a smooth term
     evaluated at the original  covariate values and let V_f denote the
     corresponding Bayesian covariance matrix. Let  V*_f denote the
     rank r pseudoinverse of V_f, where r is the  EDF for the term,
     rounded up, (or the numerical rank of V_f if this is smaller). The
     statistic used is then 

                             T = f'V*_f f

     (this can be calculated efficiently without forming the
     pseudoinverse explicitly). T is compared to a  chi-squared
     distribution with degrees of freedom given by the EDF for the term
     + 0.5,  or T is used as a component in an F ratio statistic if the
     scale parameter has been estimated. The heuristic justification
     for the reference DoF is that, away from the lower bound on the
     EDF, the theoretically correct reference level appears to be r,
     which if on average close to EDF + 0.5. However EDF + 0.5 behaves
     better than r  when the EDF is near its lower limit.

     The definition of Bayesian p-value used is:  the probability of
     observing an f less probable than bf 0, under the approximation
     for the posterior  for f implied by the truncation used in the
     test statistic.

_V_a_l_u_e:

     'summary.gam' produces a list of summary information for a fitted
     'gam' object. 

 p.coeff: is an array of estimates of the strictly parametric model
          coefficients.

     p.t: is an array of the 'p.coeff''s divided by their standard
          errors.

    p.pv: is an array of p-values for the null hypothesis that the
          corresponding parameter is zero.  Calculated with reference
          to the t distribution with the estimated residual degrees of
          freedom for the model fit if the dispersion parameter has
          been estimated, and the standard normal if not.

       m: The number of smooth terms in the model.

  chi.sq: An array of test statistics for assessing the significance of
          model smooth terms. See details.

    s.pv: An array of approximate p-values for the null hypotheses that
          each smooth term is zero. Be warned, these are only
          approximate.

      se: array of standard error estimates for all parameter
          estimates.

    r.sq: The adjusted r-squared for the model. Defined as the
          proportion of variance explained, where original variance and
           residual variance are both estimated using unbiased
          estimators. This quantity can be negative if your model is
          worse than a one  parameter constant model, and can be higher
          for the smaller of two nested models! Note that proportion
          null deviance  explained is probably more appropriate for
          non-normal errors.

dev.expl: The proportion of the null deviance explained by the model.

     edf: array of estimated degrees of freedom for the model terms.

residual.df: estimated residual degrees of freedom.

       n: number of data.

     gcv: minimized GCV score for the model, if GCV used.

    ubre: minimized UBRE score for the model, if UBRE used.

   scale: estimated (or given) scale parameter.

  family: the family used.

 formula: the original GAM formula.

dispersion: the scale parameter.

pTerms.df: the degrees of freedom associated with each parameteric term
          (excluding the constant).

pTerms.chi.sq: a Wald statistic for testing the null hypothesis that
          the each parametric term is zero.

pTerms.pv: p-values associated with the tests that each term is zero.
          For penalized fits these are approximate. The reference
          distribution  is an appropriate chi-squared when the scale
          parameter is known, and is based on an F when it is not.

cov.unscaled: The estimated covariance matrix of the parameters (or
          estimators if 'freq=TRUE'), divided by scale parameter.

cov.scaled: The estimated covariance matrix of the parameters
          (estimators if 'freq=TRUE').

 p.table: significance table for parameters

 s.table: significance table for smooths

 p.Terms: significance table for parametric model terms

_W_A_R_N_I_N_G:

     The p-values are approximate.

_A_u_t_h_o_r(_s):

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

_R_e_f_e_r_e_n_c_e_s:

     Nychka (1988) Bayesian Confidence Intervals for Smoothing Splines.
      Journal of the American Statistical Association 83:1134-1143.

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

_S_e_e _A_l_s_o:

     'gam', 'predict.gam', 'gam.check', 'anova.gam'

_E_x_a_m_p_l_e_s:

     library(mgcv)
     set.seed(0)
     dat <- gamSim(1,n=200,scale=2) ## simulate data

     b <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),data=dat)
     plot(b,pages=1)
     summary(b)

     ## now check the p-values by using a pure regression spline.....
     b.d <- round(summary(b)$edf)+1 ## get edf per smooth
     b.d <- pmax(b.d,3) # can't have basis dimension less than 3!
     bc<-gam(y~s(x0,k=b.d[1],fx=TRUE)+s(x1,k=b.d[2],fx=TRUE)+
             s(x2,k=b.d[3],fx=TRUE)+s(x3,k=b.d[4],fx=TRUE),data=dat)
     plot(bc,pages=1)
     summary(bc)

     ## p-value check - increase k to make this useful!
     n<-200;p<-0;k<-20
     for (i in 1:k)
     { b<-gam(y~s(x,z),data=data.frame(y=rnorm(n),x=runif(n),z=runif(n)))
       p[i]<-summary(b)$s.p[1]
     }
     plot(((1:k)-0.5)/k,sort(p))
     abline(0,1,col=2)

