
This page contains R code implementing a robust fit for Generalized Additive
Models, as proposed in: Azadeh, A. and SalibianBarrera, M. (2011).
An outlierrobust fit for Generalized Additive Models with applications
to disease outbreak detection. To appear in the Journal of the
American Statistical Association
(a preliminary version of the manuscript is available here).
 NEW R
package implementing this method is
available
here (link to CRAN).
 This package needs the following two packages to be present in your machine:
 This is joint work with
Davor Cubranic
 IMPORTANT DISCLAIMER:
This is a preliminarly version of the code. Limitations of the
current version that I'm aware of include:
 Leaveoneout crossvalidation as it is currently
implemented needs
a single covariate without ties. This is generally
easy to ensure by adding very small noise to the covariate.
See the example on the
rgam help page.
 An example script
to illustrate the use of the above package is available
here.
 Running the script above generates these plots (click on them to
open):
 Poisson example (1 covariate)
 Binomial example (1 covariate)
 Poisson example (2 covariates)
 True mean surface + points
 GAM estimated mean surface + points
 rgam estimated mean surface + points
 Residuals plot

