[R] package mgcv - predict with bam: Error in X[ind, ] : subscript out of bounds
Simon Wood
s.wood at bath.ac.uk
Mon Feb 3 11:19:21 CET 2014
> I suppose there may be an error of sorts, but have you considered
> the fact that solving the error might not gain you admittance into
> heaven? Look at the RHS of the model:
>
> sensor2 + s(site, bs = "re")
>
> ... and think about the fact that you are "smoothing" a factor
> variable.
- Actually this is ok. mgcv exploits the duality between quadratically
penalized smooths and Gaussian random effects to allow random effects to
be specified this way. bs="re" specifies a Gaussian random effect with
corresponding model matrix given by model.matrix(~site-1). (More
generally s(x,y,z,bs="re") specifies a gaussian random effect with model
matrix given by model.matrix(~x:y:z-1), with obvious generalization to
more or fewer variables). See mgcv help file ?random.effects for more.
best,
Simon
>> str(gapData)
> 'data.frame': 2304 obs. of 5 variables: $ sensor1 : num NA NA NA
> NA NA NA NA NA NA NA ... $ site : Factor w/ 9 levels
> "KRB","NP.FOR",..: 3 3 3 3 3 3 3 3 3 3 ... $ NthSampling: int 7489
> 7490 7491 7492 7493 7494 7495 7496 7497 7498 ... $ YDay : num
> 53 53 53 53 53 53 53 53 53 53 ... $ sensor2 : num 0.567 0.566
> 0.567 0.567 0.569 ...
>
> I'm having trouble making any sense of how that might work. It is, of
> course, possible to just do this:
>
> xylemRohWeekXnnSite.fit <- predict.gam(xylemRohWeekXnn.fit.bam,
> type="response", se=F)
>
> That gives predictions for the original dataset.
>
> But I think the error might be helpful in alerting one to the
> problems with the model.
>
--
Simon Wood, Mathematical Science, University of Bath BA2 7AY UK
+44 (0)1225 386603 http://people.bath.ac.uk/sw283
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