[R-sig-Geo] 3-year position available in Norway on modelling the distribution of marine species and habitats
Maybe this is of interest to the folks in this list. My apologies for cross-posting! We have an exciting, 3-year position available at the Institute of Marine Research in Norway under a new project aiming to develop coastal zone maps of species and habitats. You will find all the information that you need on this link: https://www.jobbnorge.no/en/available-jobs/job/194806/vacant-3-year-research-position-in-the-marine-coastal-zone-mapping-project Thanks, and feel free to redistribute! Genoveva Gonzalez Mirelis, Scientist Institute of Marine Research Nordnesgaten 50 5005 Bergen, Norway Phone number +47 55238510 [[alternative HTML version deleted]] ___ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo
Re: [R-sig-Geo] [R-sig-eco] raster::predict example doesn't work?
Thank you Ákos, your solution does work. One caveat, though: the 'predict'
function from 'stats' returns a matrix, whereas that from the 'raster' package
returns a raster object of identical extent and resolution as the supplied
predictor stack, which is very handy. If no other solutions arise I will just
convert that matrix to a raster as follows:
r <- raster(ncol=ncol(logo), nrow=nrow(logo))
extent(r) <- extent(logo)
values(r) <- pc
-Original Message-
From: R-sig-ecology On Behalf Of
Bede-Fazekas Ákos
Sent: 4. desember 2018 16:50
To: r-sig-ecol...@r-project.org; R-sig-geo
Subject: Re: [R-sig-eco] raster::predict example doesn't work?
Dear Genoveva,
All the examples in the help file of package 'party'
(https://cran.r-project.org/web/packages/party/party.pdf) use the function
predict() from package 'stats' instead of package 'raster'.
But converting the RasterStack to data.frame, changing the column 'red'
from numeric to factor, using stats::predict(..., newdata), and create a new
layer of the RasterStack can solve the problem.
logo_df <- as.data.frame(logo)
logo_df$red <- factor(logo_df$red, levels = levels(v$red)) pc <-
stats::predict(m, OOB = TRUE, newdata = logo_df) logo$pc <- pc
HTH,
Ákos Bede-Fazekas
Hungarian Academy of Sciences
2018.12.04. 15:52 keltezéssel, Gonzalez-Mirelis, Genoveva írta:
> Dear list,
>
> I posted this question recently on another list so I apologize for any
> cross-posting. Still no solution.
>
>
> I would like to use the 'predict' function in the 'raster' package in an
> implementation of species distribution modelling with a couple of factor
> variables. Furthermore, I would like to set this up exactly as the cforest
> example listed in the help file. Unfortunately, I cannot get the example to
> work!
>
>
>
> # create a RasterStack or RasterBrick with with a set of predictor
> layers
>
>
>
> logo <- brick(system.file("external/rlogo.grd", package="raster"))
>
> names(logo)
>
>
>
> # known presence and absence points
>
> p <- 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)
>
>
>
> a <- matrix(c(22, 33, 64, 85, 92, 94, 59, 27, 30, 64, 60, 33, 31, 9,
>
>99, 67, 15, 5, 4, 30, 8, 37, 42, 27, 19, 69, 60, 73, 3,
> 5, 21,
>
>37, 52, 70, 74, 9, 13, 4, 17, 47), ncol=2)
>
>
>
> # extract values for points
>
> xy <- rbind(cbind(1, p), cbind(0, a))
>
> v <- data.frame(cbind(pa=xy[,1], extract(logo, xy[,2:3])))
>
>
>
> # cforest (other Random Forest implementation) example with factors
> argument
>
>
>
> v$red <- as.factor(round(v$red/100))
>
> logo$red <- round(logo[[1]]/100)
>
>
>
> library(party)
>
> m <- cforest(pa~., control=cforest_unbiased(mtry=3), data=v) f <-
> list(levels(v$red))
>
> names(f) <- 'red'
>
> pc <- predict(logo, m, OOB=TRUE, factors=f)
>
>
>
> # Error in v[cells, ] <- predv :
>
> # number of items to replace is not a multiple of replacement length
>
>
>
> # If you change the order of the first two arguments (I read this somewhere)
> then the error changes, like this:
>
>
>
> pc <- predict(m, logo, OOB=TRUE, factors=f)
>
>
>
> # Error in RET@prediction_weights(newdata = newdata, mincriterion =
> mincriterion, :
>
> # unused argument (factors = f)
>
>
>
> # Lastly, if I run the line without the 'factors' argument
>
> pc <- predict(m, logo, OOB=TRUE)
>
>
>
> # Then I get no errors, but I don't understand the result. It's a
> vector of 40 values (predictions?)
>
>
>
> I am using Package 'raster' version 2.6-7 in RStudio 1.1.453, running off a
> server. I have tried the on several other computers, though, and the error
> persisted.
>
>
>
> Many thanks for any help,
>
>
>
> Genoveva
>
> Genoveva Gonzalez Mirelis, Scientist
> Institute of Marine Research
> Nordnesgaten 50
> 5005 Bergen, Norway
> Phone number +47 55238510
>
>
> Genoveva Gonzalez Mirelis, Scientist
> Institute of Marine Research
> Nordnesgaten 50
> 5005 Bergen, Norway
> Phone number +47 55238510
>
>
> [[alternative HTML version deleted]]
>
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>
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[R-sig-Geo] Unable to use factors in raster::predict
Dear list,
I would like to use the 'predict' function in the 'raster' package in an
implementation of species distribution modelling with a couple of factor
variables. My case can be set up exactly as the cforest example listed in the
help file. Unfortunately, I cannot get the example to work: it throws an error
because of the unused argument 'factors', which I need. Here is the code I am
referring to:
# create a RasterStack or RasterBrick with with a set of predictor layers
logo <- brick(system.file("external/rlogo.grd", package="raster"))
names(logo)
# known presence and absence points
p <- 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)
a <- matrix(c(22, 33, 64, 85, 92, 94, 59, 27, 30, 64, 60, 33, 31, 9,
99, 67, 15, 5, 4, 30, 8, 37, 42, 27, 19, 69, 60, 73, 3, 5, 21,
37, 52, 70, 74, 9, 13, 4, 17, 47), ncol=2)
# extract values for points
xy <- rbind(cbind(1, p), cbind(0, a))
v <- data.frame(cbind(pa=xy[,1], extract(logo, xy[,2:3])))
# cforest (other Random Forest implementation) example with factors argument
v$red <- as.factor(round(v$red/100))
logo$red <- round(logo[[1]]/100)
library(party)
m <- cforest(pa~., control=cforest_unbiased(mtry=3), data=v)
f <- list(levels(v$red))
names(f) <- 'red'
pc <- predict( m,logo, OOB=TRUE, factors=f)
Error in RET@prediction_weights(newdata = newdata, mincriterion = mincriterion,
:
unused argument (factors = f)
Note that I needed to change the order in the arguments in the last line. If I
run it the way it was in the example, like this:
pc <- predict(logo, m, OOB=TRUE, factors=f)
then the error is the following:
Error in v[cells, ] <- predv :
number of items to replace is not a multiple of replacement length
Also note that if I run the line without the 'factors' argument the resulting
value (r) is a matrix, and not a raster object.
I am using Package 'raster' version 2.6-7 on Windows 7 (where this worked fine
in the past).
Many thanks for any help,
Genoveva
Genoveva Gonzalez Mirelis, Scientist
Institute of Marine Research
Nordnesgaten 50
5005 Bergen, Norway
Phone number +47 55238510
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Re: [R-sig-Geo] problem with predict() in package raster and factor variables
Dear Frede and list,
My sincere apologies for not providing sufficient information or reproducible
example. As a matter of fact, you are right and when I looked further into the
data I used to estimate the random forest model I solved the problem myself! In
case it's of interest, the problem was that the variable had to be converted to
a factor *before* fitting the model, so that the result of str(v) should not be
what I showed in my original mail, but instead it should be:
'data.frame': 1257 obs. of 15 variables:
$ RefNo : int 16 16 16 16 17 17 17 17 18 18 ...
$ PointID: int 1 2 3 4 5 6 7 8 9 10 ...
$ Count : int 0 0 0 0 0 0 0 0 0 0 ...
$ PA : int 0 0 0 0 0 0 0 0 0 0 ...
$ split : chr "T" "T" "T" "T" ...
$ bathy20_1 : num 256 260 252 266 281 ...
$ TerClass : Factor w/ 6 levels "1","2","3","4",..: 2 2 1 1 1 2 1 1 3 3 ...
etc
Note that before, $TerClass was num, and now it's Factor w/ 6 levels
And f should look like this:
$TerClass
[1] "1" "2" "3" "4" "5" "6"
Then the predict() function works without any problems!
Furthermore, there is an example in the help file that exactly represents my
case, namely this bit:
# create a RasterStack or RasterBrick with with a set of predictor layers
logo <- brick(system.file("external/rlogo.grd", package="raster"))
# known presence and absence points
p <- matrix(c(48, 48, 48, 53, 50, 46, 54, 70, 84, 85, 74, 84, 95, 85,
66, 42, 26, 4, 19, 17, 7, 14, 26, 29, 39, 45, 51, 56, 46, 38, 31,
22, 34, 60, 70, 73, 63, 46, 43, 28), ncol=2)
a <- matrix(c(22, 33, 64, 85, 92, 94, 59, 27, 30, 64, 60, 33, 31, 9,
99, 67, 15, 5, 4, 30, 8, 37, 42, 27, 19, 69, 60, 73, 3, 5, 21,
37, 52, 70, 74, 9, 13, 4, 17, 47), ncol=2)
# extract values for points
xy <- rbind(cbind(1, p), cbind(0, a))
v1 <- data.frame(cbind(pa=xy[,1], extract(logo, xy[,2:3])))
# cforest (other Random Forest implementation) example with factors argument
v1$red <- as.factor(round(v1$red/100))
logo$red <- round(logo[[1]]/100)
library(party)
m <- cforest(pa~., control=cforest_unbiased(mtry=3), data=v1)
f1 <- list(levels(v1$red))
names(f1) <- 'red'
pc <- predict(logo, m, OOB=TRUE, factors=f1)
Thank you all very much, and my apologies for wasting anyone's time.
Genoveva
From: Frede Aakmann Tøgersen <fr...@vestas.com>
Sent: Sunday, May 1, 2016 6:42 AM
To: Gonzalez-Mirelis Genoveva; r-sig-geo@r-project.org
Subject: RE: [R-sig-Geo] problem with predict() in package raster and factor
variables
Hi Genoveva
You haven't got a response to your question mainly due to a) missing
information and b) missing reproducible example.
If you had provided the missing information I guess you would have solved the
problem yourself.
I have never used raster::predict() but having a look at man for that function
and you error message there is probably some differences between the data used
to estimate the random forest model (you call that a subset of the object 'v')
and the data in 'subbrick'. You should provide the structure of data used to
fit the random forest model and 'subbrick':
> str(v)
> str(subbrick)
Please also show all the relevant R code to obtain what you want in case the
error message is not related to difference in the creation of the subset of 'v'
and 'subbrick'
Yours sincerely / Med venlig hilsen
Frede Aakmann Tøgersen
Specialist, M.Sc., Ph.D.
Plant Performance & Modeling
Technology & Service Solutions
T +45 9730 5135
M +45 2547 6050
fr...@vestas.com
http://www.vestas.com
Company reg. name: Vestas Wind Systems A/S
This e-mail is subject to our e-mail disclaimer statement.
Please refer to www.vestas.com/legal/notice
If you have received this e-mail in error please contact the sender.
-Original Message-
From: R-sig-Geo [mailto:r-sig-geo-boun...@r-project.org] On Behalf Of
Gonzalez-Mirelis Genoveva
Sent: 30. april 2016 12:33
To: r-sig-geo@r-project.org
Subject: [R-sig-Geo] problem with predict() in package raster and factor
variables
Dear list,
I am trying to use the function predict() (in package raster), where I supply:
the new data as a RasterBrick, the model (as fit in previous steps and using a
different dataset), and a few more arguments including the levels of my only
one categorical value. Here is the code I'm using:
r1 <- predict(subbrick,
CIF.pa,
type="response", OOB=T, factors=f)
But I keep getting the following error:
Error in checkData(oldData, RET) :
Classes of new data do not match original data
Here are more details:
> CIF.pa
Random Forest using Conditional Inference Trees
Number of trees: 1000
Response: PA
Inputs: bathy20_1, TerClass, Smax_an
[R-sig-Geo] problem with predict() in package raster and factor variables
Dear list, I am trying to use the function predict() (in package raster), where I supply: the new data as a RasterBrick, the model (as fit in previous steps and using a different dataset), and a few more arguments including the levels of my only one categorical value. Here is the code I'm using: r1 <- predict(subbrick, CIF.pa, type="response", OOB=T, factors=f) But I keep getting the following error: Error in checkData(oldData, RET) : Classes of new data do not match original data Here are more details: > CIF.pa Random Forest using Conditional Inference Trees Number of trees: 1000 Response: PA Inputs: bathy20_1, TerClass, Smax_ann, Smean_ann, Smin_ann, SPDmax_ann, SPDmean_ann, Tmax_ann, Tmean_ann, Tmin_ann Number of observations: 986 Where 'TerClass' is a categorical variable. Here is the data used to train CIF.pa: > str(v) 'data.frame': 1257 obs. of 15 variables: $ RefNo : int 16 16 16 16 17 17 17 17 18 18 ... $ PointID: int 1 2 3 4 5 6 7 8 9 10 ... $ Count : int 0 0 0 0 0 0 0 0 0 0 ... $ PA : int 0 0 0 0 0 0 0 0 0 0 ... $ split : chr "T" "T" "T" "T" ... $ bathy20_1 : num 256 260 252 266 281 ... $ TerClass : num 2 2 1 1 1 2 1 1 3 3 ... $ Smax_ann : num 35.1 35.1 35.1 35.1 35.1 ... $ Smean_ann : num 35.1 35.1 35.1 35.1 35.1 ... $ Smin_ann : num 34.9 34.9 34.9 34.9 35 ... $ SPDmax_ann : num 0.379 0.376 0.378 0.372 0.352 ... $ SPDmean_ann: num 0.14 0.137 0.14 0.132 0.12 ... $ Tmax_ann : num 6.97 6.92 7.04 6.87 6.68 ... $ Tmean_ann : num 5.76 5.73 5.79 5.71 5.54 ... $ Tmin_ann : num 4.41 4.32 4.52 4.25 4.07 ... But actually, I used a subset of v to train the model, that where v$split=='T' Below are the values and class for TerClass for that subset > unique(v[v$split=='T',7]) [1] 2 1 3 4 6 5 > class(v$TerClass) [1] "numeric" And below are the values and class for the corresponding layer of the RasterBrick: > unique(values(subbrick$TerClass)) [1] 3 1 2 4 5 6 > class(values(subbrick$TerClass)) [1] "numeric" And finally, here is what f looks like: > f $TerClass [1] 2 1 3 4 6 5 > class(f) [1] "list" As far as I can see the classes in OldData and NewData should be the same, but the error persists. Any ideas on what I could be missing? Unfortunately I am unable to reproduce the problem (I only encounter it when using my data), but any help will be hugely appreciated Also, I am aware that I asked this question before (Apr 04, 2013; 1:22pm). Unfortunately I haven't gotten very far since then! Many thanks in advance for any pointers. Genoveva ___ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo
Re: [R-sig-Geo] help- raster overlap (Charbel Eliane)
Hi Eliane,
Not sure that it will work, but I would recommend converting your raster layers
to .tiff files prior to importing them into R. Then try
aa-raster('C:/QA_soil/inttotalrain.tif',datatype=INT1U)
(note the file extension, and data type argument).
Good luck!
Geno
hi list,
i am using R 2.15.3 (64 bit)
i want to stack several raster objects to use for prediction purposes. All
my raster files are integer and has attribute tables and none has NA
values. ( i checked it in arcgis).however when i try to import the raster
to R and check the values in each raster, the only values that i get are
NAs. I hope the example below will clarify more the problem i am facing.
Has anymore encountered a similar problem before? i appreciate any
suggestion. Thanks in advance.
PS: i am able to stack the raster files as well as plot them or compare
them and even predict with it. however with predict i get the message:
Error in .getRat(ratvalues, ratnames, rattypes) :
argument rattypes is missing, with no default
example:
a-C:/QA_soil/inttotalrain
aa-raster(a)
head(aa)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
7 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
8 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
9 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
tail(aa)
15310 15311 15312 15313 15314 15315 15316 15317 15318 15319 15320
15321
18689NANANANANANANANANANANA
NA
18690NANANANANANANANANANANA
NA
18691NANANANANANANANANANANA
NA
18692NANANANANANANANANANANA
NA
18693NANANANANANANANANANANA
NA
18694NANANANANANANANANANANA
NA
18695NANANANANANANANANANANA
NA
18696NANANANANANANANANANANA
NA
18697NANANANANANANANANANANA
NA
18698NANANANANANANANANANANA
NA
15322 15323 15324 15325 15326 15327 15328 15329
18689NANANANANANANANA
18690NANANANANANANANA
18691NANANANANANANANA
18692NANANANANANANANA
18693NANANANANANANANA
18694NANANANANANANANA
18695NANANANANANANANA
18696NANANANANANANANA
18697NANANANANANANANA
18698NANANANANANANANA
aa
class : RasterLayer
dimensions : 18698, 15329, 286621642 (nrow, ncol, ncell)
resolution : 10, 10 (x, y)
extent : 685798.3, 839088.3, 3658405, 3845385 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=utm +zone=36 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0
+units=m +no_defs
data source : C:\QA_soil\inttotalrain
names : inttotalrain
values : 6, 1467 (min, max)
attributes :
ID COUNT
from:6 285
to : 146755
On Tue, Feb 19, 2013 at 3:08 PM, Charbel Eliane enchar...@gmail.com wrote:
Dear list,
I have 2 raster that represent exactly the same area. the 2 raster have
the same coordinate system and the same resolution but 2 different origins
and extents. They are both float. My goal is to perfectly overlap the 2
rasters, so i can use it for analysis.
I have tried to use resample in R, to resample one raster using the other
but the output raster doesn't perfectly overlap with the raster that i used
to resample to [y in resample (x,y)]. the dim of the resultant resampled
raster and the y raster are the same except for the number of cells.
I will be grateful for any help.
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Re: [R-sig-Geo] seemingly unresolved problem with predict() in package raster
Many thanks for looking into this Robert! From: Robert J. Hijmans [mailto:r.hijm...@gmail.com] Sent: April-04-13 17:53 To: Gonzalez-Mirelis Genoveva Cc: r-sig-geo@r-project.org Subject: Re: [R-sig-Geo] seemingly unresolved problem with predict() in package raster cforest with factors are currently not supported (although they may work in some cases). I will change the predict function to make it more general by adding a 'levels' argument such that you can indicate (for models with a non-standard structure such as cforest) which variables are factors and what their levels are. Robert On Thu, Apr 4, 2013 at 4:22 AM, Gonzalez-Mirelis Genoveva genoveva.gonzalez-mire...@imr.nomailto:genoveva.gonzalez-mire...@imr.no wrote: Hi all, I have a problem with the function raster::predict very similar to the one described here [1], [2] using raster package version 2.0-41 and party package version 1.0-6, where my model is a conditional inference forest (party::cforest). Could not find a solution in either post. The problem is the following: when I use the function predict() I get an error relating to a mismatch between the levels of factors in the data frame used to fit the model and those of the raster layers used to predict to the whole surface. The only difference that I can see is that the levels of the factors in the model are as follows (e.g.): morphrec.levels-levels(v$morphrec) morphrec.levels [1] 1 2 3 4 5 6 Whereas if I ask for the unique values of the same raster layer (where values were extracted from in an earlier step) that is now being used to predict a value for the response variable, I see the following: morphrec.values-sort(unique(getValues(morphrec))) morphrec.values [1] 1 2 3 4 5 6 When I try running predict, the following happens: r - predict(predictors, confor.dens, type='response',OOB=TRUE) Loading required package: tcltk Loading Tcl/Tk interface ... done Error in checkData(oldData, RET) : Classes of new data do not match original data predictors class : RasterStack dimensions : 2787, 2293, 6390591, 7 (nrow, ncol, ncell, nlayers) resolution : 200, 200 (x, y) extent : 296387.5, 754987.5, 7488413, 8045813 (xmin, xmax, ymin, ymax) coord. ref. : +proj=utm +zone=33 +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0 names : bathy, slope, tri, grainsize, landscape, sedenv,morphrec min values : 0.44850001, 0.02432192, 0.04429007, 1., 21., 1., 1. max values : 2912.94849,46.13418,43.59974, 300.0, 431.0, 5.0, 6.0 confor.dens Random Forest using Conditional Inference Trees Number of trees: 1000 Response: density Inputs: bathy, slope, tri, grainsize, landscape, sedenv, morphrec Number of observations: 1020 Is there any way of telling predict() that the values of some raster layers will have to be considered characters? Or am I facing a different problem entirely? Any help will be much appreciated. I have been working on creating a small, reproducible example, but I can't seem to get the exact same behavior. Regards, Geno [1] http://r-sig-geo.2731867.n2.nabble.com/problems-with-predict-in-package-raster-td6627291.html#a6628041 [2] http://r-sig-geo.2731867.n2.nabble.com/new-function-predict-package-raster-td5796744.html#a5816769 Genoveva Gonzalez Mirelis, Scientist Institute of Marine Research Nordnesgaten 50 5005 Bergen, Norway +47 5523 6376tel:%2B47%205523%206376 [[alternative HTML version deleted]] ___ R-sig-Geo mailing list R-sig-Geo@r-project.orgmailto:R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo [[alternative HTML version deleted]] ___ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo
[R-sig-Geo] seemingly unresolved problem with predict() in package raster
Hi all, I have a problem with the function raster::predict very similar to the one described here [1], [2] using raster package version 2.0-41 and party package version 1.0-6, where my model is a conditional inference forest (party::cforest). Could not find a solution in either post. The problem is the following: when I use the function predict() I get an error relating to a mismatch between the levels of factors in the data frame used to fit the model and those of the raster layers used to predict to the whole surface. The only difference that I can see is that the levels of the factors in the model are as follows (e.g.): morphrec.levels-levels(v$morphrec) morphrec.levels [1] 1 2 3 4 5 6 Whereas if I ask for the unique values of the same raster layer (where values were extracted from in an earlier step) that is now being used to predict a value for the response variable, I see the following: morphrec.values-sort(unique(getValues(morphrec))) morphrec.values [1] 1 2 3 4 5 6 When I try running predict, the following happens: r - predict(predictors, confor.dens, type='response',OOB=TRUE) Loading required package: tcltk Loading Tcl/Tk interface ... done Error in checkData(oldData, RET) : Classes of new data do not match original data predictors class : RasterStack dimensions : 2787, 2293, 6390591, 7 (nrow, ncol, ncell, nlayers) resolution : 200, 200 (x, y) extent : 296387.5, 754987.5, 7488413, 8045813 (xmin, xmax, ymin, ymax) coord. ref. : +proj=utm +zone=33 +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0 names : bathy, slope, tri, grainsize, landscape, sedenv,morphrec min values : 0.44850001, 0.02432192, 0.04429007, 1., 21., 1., 1. max values : 2912.94849,46.13418,43.59974, 300.0, 431.0, 5.0, 6.0 confor.dens Random Forest using Conditional Inference Trees Number of trees: 1000 Response: density Inputs: bathy, slope, tri, grainsize, landscape, sedenv, morphrec Number of observations: 1020 Is there any way of telling predict() that the values of some raster layers will have to be considered characters? Or am I facing a different problem entirely? Any help will be much appreciated. I have been working on creating a small, reproducible example, but I can't seem to get the exact same behavior. Regards, Geno [1] http://r-sig-geo.2731867.n2.nabble.com/problems-with-predict-in-package-raster-td6627291.html#a6628041 [2] http://r-sig-geo.2731867.n2.nabble.com/new-function-predict-package-raster-td5796744.html#a5816769 Genoveva Gonzalez Mirelis, Scientist Institute of Marine Research Nordnesgaten 50 5005 Bergen, Norway +47 5523 6376 [[alternative HTML version deleted]] ___ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo
