cv.glm                 package:boot                 R Documentation

Cross-validation for Generalized Linear Models

Description:

     This function calculates the estimated K-fold cross-validation
     prediction  error for generalized linear models.

Usage:

     cv.glm(data, glmfit, cost, K)

Arguments:

    data: A matrix or data frame containing the data.  The rows should
          be cases and the columns correspond to variables, one of
          which is the response. 

  glmfit: An object of class '"glm"' containing the results of a
          generalized linear model fitted to 'data'. 

    cost: A function of two vector arguments specifying the cost
          function for the  cross-validation.  The first argument to
          'cost' should correspond to the observed responses and the
          second argument should correspond to the predicted or fitted
          responses from the generalized linear model.  'cost' must
          return a non-negative scalar value.  The defualt is the
          average squared error function. 

       K: The number of groups into which the data should be split to
          estimate the cross-validation prediction error.  The value of
          'K' must be such that all groups are of approximately equal
          size.  If the supplied value of 'K' does not satisfy this
          criterion then it will be set to the closest integer which
          does and a warning is generated specifying the value of 'K'
          used.  The default is to set 'K' equal to the number of
          observations in 'data' which gives the usual leave-one-out
          cross-validation. 

Details:

     The data is divided randomly into 'K' groups.  For each group the
     generalized linear model is fit to 'data' omitting that group,
     then the function 'cost' is applied to the observed responses in
     the group that was omitted from the fit and the prediction made by
     the fitted models for those observations.

     When 'K' is the number of observations leave-one-out
     cross-validation is used and all the possible splits of the data
     are used.  When 'K' is less than the number of observations the
     'K' splits to be used are found by randomly partitioning the data
     into 'K' groups of approximately equal size.  In this latter case
     a certain amount of bias is introduced.  This can be reduced by
     using a simple adjustment (see equation 6.48 in Davison and
     Hinkley, 1997). The second value returned in 'delta' is the
     estimate adjusted by this method.

Value:

     The returned value is a list with the following components.

    call: The original call to 'cv.glm'. 

       K: The value of 'K' used for the K-fold cross validation. 

   delta: A vector of length two.  The first component is the raw
          cross-validation estimate of prediction error.  The second
          component is the adjusted cross-validation estimate.  The
          adjustment is designed to compensate for the bias introduced
          by not using leave-one-out cross-validation. 

    seed: The value of '.Random.seed' when 'cv.glm' was called.  

Side Effects:

     The value of '.Random.seed' is updated.

References:

     Brieman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J. (1984)
     _Classification and Regression Trees_. Wadsworth.

     Burman, P. (1989) A comparitive study of ordinary
     cross-validation,  _v_-fold cross-validation and repeated
     learning-testing methods. _Biometrika_, *76*, 503-514

     Davison, A.C. and Hinkley, D.V. (1997)  _Bootstrap Methods and
     Their Application_. Cambridge University Press.

     Efron, B. (1986) How biased is the apparent error rate of a
     prediction rule? _Journal of the American Statistical
     Association_, *81*, 461-470.

     Stone, M.  (1974) Cross-validation choice and assessment of
     statistical predictions (with Discussion).  _Journal of the Royal
     Statistical Society, B_, *36*, 111-147.

See Also:

     'glm', 'glm.diag', 'predict'

Examples:

     # leave-one-out and 6-fold cross-validation prediction error for 
     # the mammals data set.
     data(mammals, package="MASS")
     mammals.glm <- glm(log(brain)~log(body),data=mammals)
     cv.err <- cv.glm(mammals,mammals.glm)
     cv.err.6 <- cv.glm(mammals, mammals.glm, K=6)

     # As this is a linear model we could calculate the leave-one-out 
     # cross-validation estimate without any extra model-fitting.
     muhat <- mammals.glm$fitted
     mammals.diag <- glm.diag(mammals.glm)
     cv.err <- mean((mammals.glm$y-muhat)^2/(1-mammals.diag$h)^2)

     # leave-one-out and 11-fold cross-validation prediction error for 
     # the nodal data set.  Since the response is a binary variable an
     # appropriate cost function is
     cost <- function(r, pi=0) mean(abs(r-pi)>0.5)

     data(nodal)
     nodal.glm <- glm(r~stage+xray+acid,binomial,data=nodal)
     cv.err <- cv.glm(nodal, nodal.glm, cost, K=nrow(nodal))$delta 
     cv.11.err <- cv.glm(nodal, nodal.glm, cost, K=11)$delta 

