[R-sig-Geo] Predict GAM model with categorical predictors

[Thiago Cesar Lima Silveira]

## Disregard the code sent before, this is the right

Hi,

I'm experiencing some problems to predict a 
gam model with factor as predictor to raster stack. 

I wrote an example code to illustrate the problem.

This is the error message when I try to predict with a model with factors:


'Error in `[.data.frame`(blockvals, , f[j]) : undefined columns selected

I home someone could help.

Thanks.

Thiago

___________________________________
Thiago Cesar Lima Silveira
Biologist - MSc. Zoology 
PhD candidate - Zoology PUCRS
e-mail: thiago.cesar at acad.pucrs.br <mailto:thiago.cesar at acad.pucrs.br>
Skype: thiagocesarls
CV: http://lattes.cnpq.br/5960267776845701 <http://lattes.cnpq.br/5960267776845701>



#####
library(mgcv)
library(raster)

#raster example
v1rst<-raster()
values(v1rst) <- 1:ncell(v1rst)
names(v1rst)<-'v1'

# Example of response variable and predictors
y<-c(1,33,500,700, 334,320, 703,303,3030,3002,200,0,100,100,169)
v1<-c(12,33,544,600, 34,30, 03,3390,3030,302,20,108,170,101,2009)
v2<-c(0,0,0,0,0,0,0,0,1,1,1,1,1,1,1)
v3<-c(12,33,544,600, 34,30, 03,3390,3030,302,20,108,170,101,2009)
df<-data.frame(y, v1, v2)

#GAM model with factor
gam1<-gam(y~s(v1)+factor(v2), data=df)
summary(gam1)

#GAM model without factor
gam2<-gam(y~s(v1), data=df)
summary(gam2)

# data.frame with a constant value 
#(of class ’factor’) to pass that on to the predict function.
Method<-factor( '0' ,levels=levels(df$v2))) 
add<-data.frame(Method)

#Prediction with factor
   p<-predict(v1rst,gam1, type='response', const=add )
              
#This is the error 'Error in `[.data.frame`(blockvals, , f[j]) : undefined columns selected
              
#Prediction without factor
p<-predict(v1rst,gam2, type='response')
plot(p)


	[[alternative HTML version deleted]]