My main doubts are:
1.Is the use of by and te right with the negative binomial
distribution and with the binomial distribution?
-- yes! These things specify the `linear predictor' of the model --- the
correctness of the linear predictor does not usually depend on the
response distribution.
2.Do these interactions
have the same meaning that the interaction factor*continuous covariate
and continuous covariate* continuous covariate used in the GLM?
-- Only at a rather general level. The factor*continuous case uses a separate
smooth function of the continuous variable for each level of the factor
(although you can force all the smoothing parameter to be the same). The
continuous*continous interaction uses a single smooth function of both
continuous variables as the interaction.
3.Is
right to introduce in the model the continuous covariates and the factor
moreover their interactions?
-- Your models all look potentially sensible, with the continuous and factor
variables dealt with in reasonable ways (of course I can't tell whether they
are actually appropriate for the data you have).
best,
Simon
On Friday 03 September 2010 15:06, Lucia Cañas wrote:
Hello R users,
I am working with the GAM to inspect the effect of some factors (year,
area) and continuous variables (length, depth, latitude and longitude) on
the intensity and prevalence of the common parasite Anisakis. I would like
introduce interaction in my models, both continuous variables-continuous
variables and continuous variables-factor. I have read some
questions-answers regard to this subject but I still have doubts. The
solution that I have seen to introduce an interaction continuous
covariate-factor is using by (explained in ?gam.models). Below, I show
an example of my model with the interactions using by both to prevalence
(distribution=binomial) and to intensity (distribution=negative binomial):
gam(prevalence~s(length)+factor(year)+factor(area)+s(length,by=area)+s(leng
th,by=year), family=binomial,data=X)
gam(intensity~s(length)+factor(year)+factor(area)+s(length,by=area)+s(lengt
h,by=year), family=negbin(c(1,10)),data=X)
The solution that I have seen to introduce an interaction continuous
covariate- continuous covariate is using the function te. Below, I show
an example of my model with the interactions using te both to prevalence
(distribution=binomial) and to intensity (distribution=negative binomial):
gam(prevalence~s(length)+s(depth)+s(latitude)+s(longitude)+te(depth,length)
+ te(latitude,length)+ te(longitude,length),family=binomial,data=X)
gam(intensity~s(length)+s(depth)+s(latitude)+s(longitude)+te(depth,length)+
te(latitude,length)+ te(longitude,length),family= negbin(c(1,10)),data=X)
Thanks in advance.
Best regards,
Luc�a Ca��s
Luc�a Ca��s Ferreiro
Instituto Espa�ol de Oceanograf�a
Centro Oceanogr�fico de A Coru�a
Paseo Mar�timo Alcalde Francisco V�zquez, n� 10
15001 - A Coru�a, SPAIN
e-mail: lucia.ca...@co.ieo.es
Tel: +34981205362; Fax: +34981229077
http://www.ieo.es
[[alternative HTML version deleted]]
--
Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK
+44 1225 386603 www.maths.bath.ac.uk/~sw283
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