Rolf Turner wrote:
It seems that in general
gam(y~lo(x)) # gam() from the gam package.
and
loess(y~x)
give slightly different results (in respect of the predicted/fitted
values).
Most noticeable at the endpoints of the range of x.
Can anyone enlighten me about the reason for this difference?
Is it possible to twiddle the control parameters, for either or both
functions,
so as to obtain identical results?
There are two obvious differences in the defaults. In lo() from the gam
package, span=0.5 and degree=1 while for loess(), span=0.75 and degree=2.
Try gam(y~lo(x,span=0.75,degree=2)) and see if that helps.
Kevin
--
Kevin E. Thorpe
Biostatistician/Trialist, Knowledge Translation Program
Assistant Professor, Dalla Lana School of Public Health
University of Toronto
email: kevin.tho...@utoronto.ca Tel: 416.864.5776 Fax: 416.864.6057
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