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

Robert J. Hijmans r.hijmans at gmail.com
Wed Dec 8 22:02:08 CET 2010 

Isabelle, I think I fixed this in raster 1.7-11. Robert

On Wed, Dec 8, 2010 at 8:55 AM, isabelle boulangeat
<isabelle.boulangeat at gmail.com> wrote:
> Dear Robert,
>
> I am afraid this doesn't work with a "randomForest" model type (package
> randomForest), although there is no problem without categorical variables.
>
> To be continued...
>
> Isabelle.
>
> 2010/12/6 isabelle boulangeat <isabelle.boulangeat at gmail.com>
>>
>> Dear Robert,
>>
>> I tested today my script with the package 1.7-9 and it works!
>>
>> Pred_fac <- predict(rasStack, model=model_fac, predict = predict.glm,
>> type="response", progress='text')
>>
>> Thanks a lot.
>>
>> Isabelle.
>>
>> 2010/12/3 Robert J. Hijmans <r.hijmans at gmail.com>
>>>
>>> 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/
>>> >
>>
>>
>>
>> --
>> 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/
>
>
>
> --
> 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/
>

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