The 'scat' (scaled t) family in mgcv does allow for much heavier tails
than Gaussian, but it may not be robust enough for you.
On 25/02/15 15:27, Ilaria Prosdocimi wrote:
Ilaria Prosdocimi <ilapro <at> ceh.ac.uk> writes:
Christos Giannoulis <cgiannoul <at> gmail.com> writes:
Dear All,
I was looking the r-archives and crantastic...for a package that has
a
robust approach to generalized additive models. I found two packages
"robustgam" and "rgam" but their implemented functions
cover only binomial and poisson distributions (pls correct me if I
am
wrong).
I would greatly appreciate if anyone could share with us other
packages or
robust approaches of general additive modeling that might have a
better
performance with small data sets (n = 50 -100 records).
Thank you very much all for reading this message. I am hoping and
looking
forward to receiving your reply.
Sincerely,
Christos Giannoulis
[[alternative HTML version deleted]]
Indeed, it seems that both the libraries do not allow normal data. I
think
you could try to use the code in this page
(http://www.stat.ubc.ca/~matias/penalised/) for S-estimation.
If you want the code for the Croux et al paper
(http://onlinelibrary.wiley.com/doi/10.1111/j.1541-
0420.2011.01630.x/full)
just email me (ilapro + ceh.ac.uk) - it seems to be not available
online
anymore. This also allow normal data.
Best
Ilaria
For information - in case somebody finds this post again in the future,
the original files have now been restored in the KULeuven website at
this address
http://wis.kuleuven.be/stat/stat-inferen/codes
I have also packaged the code in a R package which can be accessed on
GitHub (and installed with devtools::install_github) at
https://github.com/ilapros/DoubleRobGam
Best
Ilaria
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