Thanks for the swift reply,
I might have been a bit sloppy with describing my datasets and problem. I
showed the first model as an example of the type of GAM that I had been able
to use the predict function on. What I am looking for is how to predict my
m3:
model3<-gam(y_no~s(day,by=mapID),family=binomial, data=mergeday)
When I plot this I get 8 different graphs. Each showing me a different
habitat type with on the x-axis days and on the y-axis s(day,2,81):mapID.
With predict I was hoping to get the scale of the y-axis right for a
selection of days (for example 244,304).
I have tried to reform the script you gave me to match my dataset in m3, but
it all did not seem to work.
newd2 <- data.frame(day = rep(seq(244, 304, length = 100), 8),
mapID = rep(levels(mergeday$mapID), each = 100))
newd2 <- data.frame(day = rep(seq(244, 304, length = 100), 8),
mapID = rep(sort(unique(mergeday$mapID)),
each = 100))
I am guessing it must have something to do with the by in s(day,by=mapID).
I haven't come across any examples that used a GAM with by and then used the
predict function.
(A sample of the dataset:
mapID day y_no
Urban Areas and Water 25 1
Urban Areas and Water 26 1
Early Succesional Forest 27 0
Agriculture 28 0
Early Succesional Forest 29 0
Mature Coniferous Forest 30 0)
I am sorry that I have to bother you even more with this, and I hope that my
additional explanation about my problem might help solve it.
Sincerely yours,
Robbert Langenberg
2009/1/16 Gavin Simpson <gavin.simp...@ucl.ac.uk>
> On Fri, 2009-01-16 at 12:36 +0100, Robbert Langenberg wrote:
> > Dear,
> >
> > I am trying to get a prediction of my GAM on a response type. So that I
> > eventually get plots with the correct values on my ylab.
> > I have been able to get some of my GAM's working with the example shown
> > below:
> > *
> > model1<-gam(nsdall ~ s(jdaylitr2), data=datansd)
> > newd1 <- data.frame(jdaylitr2=(244:304))
> > pred1 <- predict.gam(model1,newd1,type="response")*
>
> Hi Robert,
>
> You want predictions for the covariate over range 244:304 for each of
> your 8 mapID's, yes?
>
> This is not tested, but why not something like:
>
> newd2 <- data.frame(jdaylitr2 = rep(seq(244, 304, length = 100), 8),
> mapID = rep(levels(datansd$mapID), each = 100))
>
> Then use newd2 in your call to predict.
>
> I am assuming that datansd$mapID is a factor in the above. If it is just
> some other indicator variable, then perhaps something like:
>
> newd2 <- data.frame(jdaylitr2 = rep(seq(244, 304, length = 100), 8),
> mapID = rep(sort(unique(datansd$mapID)),
> each = 100))
>
> Does that work for you?
>
> HTH
>
> G
>
> >
> > The problem I am encountering now is that I cannot seem to get it done
> for
> > the following type of model:
> >
> > *model3<-gam(y_no~s(day,by=mapID),family=binomial, data=mergeday)*
> >
> > My mapID consists of 8 levels of which I get individual plots with *
> > plot(model3)*. When I do predict with a newdata in it just like my first
> > model I need all columns to have the same amount of rows or else R will
> not
> > except it ofcourse, the col.names need to at least include day and mapID.
> > This way I can not get a prediction working for this GAM, I am confused
> > because of this part in the model: *s(day,by=mapID).
> >
> > *I have been reading through the GAM, an introduction with R book from
> Wood,
> > S. but could not find anything about predictions with BY in the model.
> >
> > I hope someone can help me out with this,
> >
> > Sincerely yours,
> >
> > Robbert Langenberg
> >
> > [[alternative HTML version deleted]]
> >
> > ______________________________________________
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> Dr. Gavin Simpson [t] +44 (0)20 7679 0522
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