> 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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