[R-sig-Geo] new function predict, package raster

Fri Dec 3 20:59:57 CET 2010

Dear Isabelle,

Thanks for the detailed report. So it is a bug after all. This is
probably fixed in raster 1.7-8 (on CRAN for win and lin now).
At least the below works for me (model with 2 variables, one is a factor).

Robert

library(raster)
# create a RasterStack (a set of predictor rasters)
logo <- stack(system.file("external/rlogo.grd", package="raster"))
presence <- matrix(c(48, 48, 48, 53, 50, 46, 54, 70, 84, 85, 74, 84,
95, 85, 66, 42, 26, 4, 19, 17, 7, 14, 26, 29, 39, 45, 51, 56, 46, 38,
31, 22, 34, 60, 70, 73, 63, 46, 43, 28), ncol=2)
background <- cbind(runif(250)*(xmax(logo)-xmin(logo))+xmin(logo),
runif(250)*(ymax(logo)-ymin(logo))+ymin(logo))

xy <- rbind(cbind(1, presence), cbind(0, background))
v <- cbind(xy[,1], extract(logo, xy[,2:3]))
colnames(v)[1] <- 'presback'
v=data.frame(v[,1:3])
v[,3] <- as.factor(v[,3])

model <- glm(formula=presback~., data=v)
r <- predict(logo, model, progress='text')

On Fri, Dec 3, 2010 at 3:26 AM, isabelle boulangeat
<isabelle.boulangeat at gmail.com> wrote:
> Hi,
>
> Thanks for all these explanations. I misunderstood the use of the "const".
> In my case, I just need to deal with an explicative variable that is
> spatially explicit but categorial.
> I should use a raster with numbers that correspond to my factor's levels.
> However, I still have some problems to predict using the categorial variable
> (raster package, version 1.7-7)
>
> my code : predict(rasStack, model=model_fac)
> Error message : Erreur dans v[cells, ] <- predv :
>
> Here are details on the raster Stack and the data.frame containing
> explicative variables to fit the model (expl).
>
> rasStack
> class       : RasterStack
> nlayers     : 2
> nrow        : 3137
> ncol        : 1951
> ncell       : 6120287
> projection  : +proj=lcc +lat_1=45.89891888888889 +lat_2=47.69601444444444
> +lat_0=46.8 +lon_0=2.337229166666667 +x_0=600000 +y_0=2200000 +ellps=WGS84
> +datum=WGS84 +units=m +no_defs +towgs84=0,0,0
> min value   : -1280     1
> max value   : 462   7
> extent      : 784300, 979400, 1858800, 2172500  (xmin, xmax, ymin, ymax)
> resolution  : 100, 100  (x, y)
>
> layerNames(rasStack)
> [1] "temp"   "corine"
>
> unique(getValues(rasStack[[2]]))
> [1] NA  6  5  1  7  4  2  3
>
> str(expl)
> 'data.frame':   11396 obs. of  2 variables:
>  $ temp  : num  -78.4 -282.9 -172.1 -173.6 -83.2 ...
>  $ corine: Factor w/ 7 levels "1","2","3","4",..: 6 4 3 4 5 6 6 4 5 6 ...
>
> unique(expl$corine)
> [1] 6 4 3 5 7 2 1
> Levels: 1 2 3 4 5 6 7
>
> model_fac <- glm(resp ~ ., data=expl, family="binomial")
>
>
> Any idea?
>
> 2010/12/2 Robert J. Hijmans <r.hijmans at gmail.com>
>>
>> Isabelle,
>>
>> If you use a factor that is represented by numbers on a grid you can
>> just fit the model (with the factor variable as factor), and the
>> predict function will then make a factor from the predictor layers
>> (i.e. there is nothing else you need to do). Where this can go wrong
>> is if you have factor levels on your raster that were not in the data
>> used to fit the model. You can get something like:
>> Error in model.frame.default(Terms, newdata, na.action = na.action,
>> xlev = object$xlevels) :
>>  factor 'x' has new level(s):  2, 3, 4, 5,
>> Which means that you first need to remove those levels (set to NA)
>> from your predictor raster. (I will try to automate that problem away,
>> in raster::predict, eventually)
>>
>> The argument "const" is for factor type variables for which you do not
>> have spatial data. Hence they could be considered 'constant' in some
>> contexts. For example, with fish observation data you might have a
>> model that predicts abundance from a number of variables including the
>> method used to catch the fish. Say a factor variable "method" with
>> levels "A" or "B". You can then do something like
>> method = factor("A")
>> predict(raster, model, const=method)
>>
>> Robert
>>
>> On Thu, Dec 2, 2010 at 9:02 AM, isabelle boulangeat
>> <isabelle.boulangeat at gmail.com> wrote:
>> > Hello,
>> >
>> > I am trying to use the recently updated function "predict" from the
>> > package
>> > raster. I would like to use the field "const", as one of my variable is
>> > categorial.
>> > I have an error message but I can't find where is the problem.
>> >
>> > Error message : Erreur dans `[.data.frame`(blockvals, , f[i]) :
>> > undefined
>> > columns selected
>> >
>> > code : Pred_fac <- predict(rasStack, model=model_fac,
>> > predict=predict.glm,
>> > progress='text', const=factorDF)
>> >
>> > rasStack is a RasterStack and contains only one layer with one
>> > explicative
>> > variable. the layer name is the same as in the model.
>> > factorDF is a data.frame and contains only one column with one
>> > explicative
>> > variable. the column name is the same as in the model.
>> >
>> > the dimensions are the same : the number of rows in the data frame
>> > correspond to the number of cells in the raster.
>> >
>> > Does someone succeed to use this function with the new field "const" ?
>> >
>> > Thanks for help,
>> >
>> > Cheers,
>> >
>> > Isabelle.
>> >
>> > --
>> > Isabelle Boulangeat, PhD student
>> > Ecosystem dynamics and plant traits
>> >
>> > Bureau 203, +33 (0) 476 635 733
>> >
>> > Laboratoire d'écologie Alpine (LECA)
>> > Grenoble, France
>> > lab' s website : http://www-leca.ujf-grenoble.fr/
>> > my website : http://j.boulangeat.free.fr/
>> >
>> >        [[alternative HTML version deleted]]
>> >
>> >
>> > _______________________________________________
>> > R-sig-Geo mailing list
>> > R-sig-Geo at r-project.org
>> > https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>> >
>> >
>
>
>
> --
> Isabelle Boulangeat, PhD student
> Ecosystem dynamics and plant traits
>
> Bureau 203, +33 (0) 476 635 733
>
> Laboratoire d'écologie Alpine (LECA)
> Grenoble, France
> lab' s website : http://www-leca.ujf-grenoble.fr/
> my website : http://j.boulangeat.free.fr/
>