[R] combine lattice plot and standard R plot
Thank you very much for your help and for your quick answers. Lucía Cañás Ferreiro Instituto Español de Oceanografía Centro Oceanográfico de A coruña Paseo Marítimo Alcalde Francisco Vázquez, 10 15001 - A Coruña, Spain Tel: +34 981 218151 Fax: +34 981 229077 lucia.ca...@co.ieo.es http://www.ieo.es [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] combine lattice plot and standard R plot
Dear R users, I would like to combine lattice plot (xyplot) and standard R plot (plot and plotCI) in an unique figure. I use the function par() to combine plot and plotCI and I use the function print() to combine xyplot. I tried to use these functions to combine xyplot and plotCI and plots but they do not work. Does anybody know how I can do this? Thank you very much in advance. Lucía Cañás Ferreiro Instituto Español de Oceanografía Centro Oceanográfico de A coruña Paseo Marítimo Alcalde Francisco Vázquez, 10 15001 - A Coruña, Spain Tel: +34 981 218151 Fax: +34 981 229077 lucia.ca...@co.ieo.es http://www.ieo.es [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] sm.ancova graphic
Hi R-Users, I am working with sm.ancova (in the package sm) and I have two problems with the graph, which is automatically generated when sm.ancova() is run. 1-Besides of the fitted lines, the observed data appeared automatically in the graph. I prefer that only fitted lines appear. I check the sm.options, but I could not find the way that the observed data do not appear in the graph. 2-I would like to change the size of the numbers in the axis. Again, I check the sm.options, but I could not find the correct way. Thank you in advance, Lucía [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Interactions in GAM
Thank you so much for your answer, it has been really useful. I have already included the interactions in the models and I have obtained better results. Best regards, Lucía Cañás Lucía Cañás Ferreiro Instituto Español de Oceanografía Centro Oceanográfico de A Coruña Paseo Marítimo Alcalde Francisco Vázquez, nº 10 15001 - A Coruña, SPAIN e-mail: lucia.ca...@co.ieo.es Tel: +34981205362; Fax: +34981229077 http://www.ieo.es [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Interactions in GAM
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(length,by=year), family=binomial,data=X) gam(intensity~s(length)+factor(year)+factor(area)+s(length,by=area)+s(length,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) My main doubts are: 1. Is the use of by and te right with the negative binomial distribution and with the binomial distribution? 2. Do these interactions have the same meaning that the interaction factor*continuous covariate and continuous covariate* continuous covariate used in the GLM? 3. Is right to introduce in the model the continuous covariates and the factor moreover their interactions? Thanks in advance. Best regards, Lucía Cañás Lucía Cañás Ferreiro Instituto Español de Oceanografía Centro Oceanográfico de A Coruña Paseo Marítimo Alcalde Francisco Vázquez, nº 10 15001 - A Coruña, SPAIN e-mail: lucia.ca...@co.ieo.es Tel: +34981205362; Fax: +34981229077 http://www.ieo.es [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
