From pep.bioalerts at gmail.com Fri Mar 1 15:28:12 2013
From: pep.bioalerts at gmail.com (Josep M Serra diaz)
Date: Fri, 1 Mar 2013 15:28:12 +0100
Subject: [Biomod-commits] Cannot find rasters for different rules of
ensembleForecasting
Message-ID:
Dear BIOMODers,
I have been running bimod2 for my first time (great vignettes, by the way)
and I am struggling to find the rasters/rasterStack that shows different
ensembleForecasting according to different assembly rules (e.g. em.cv,
em.mean, etc.). So I ended up running the script of the example to check
whether I was confused or I did something wrong.
...and I found that after running the whole example, I cannot load either
this rasterStack.
I mean this part of the code:
###################################################
### code chunk number 19: EnsembleForecasting_loading_res
###################################################
load("Myocastor/proj_t2050/Myocastor_PA1_AllRun_EM.TSS")
Myocastor_PA1_AllRun_EM.TSS
###################################################
### code chunk number 20: EnsembleForecasting_plotting_res
###################################################
plot(Myocastor_PA1_AllRun_EM.TSS)
QUESTIONS:
1. Where can I find these different ensembles projections?
2. The assembly rule (the way the ensemble is done; mean/median/committee)
does not interfere with 'em.by' value in BIOMOD_EnsembleModeling()
function. Is that correct? I mean, we can always ask to ensamble using the
median of the predictions, or committee, but across different subsets (PA,
PA+algo, all, etc.)
Thanks,
pep
From damien.georges2 at gmail.com Fri Mar 1 15:44:51 2013
From: damien.georges2 at gmail.com (Damien Georges)
Date: Fri, 01 Mar 2013 15:44:51 +0100
Subject: [Biomod-commits] Cannot find rasters for different rules of
ensembleForecasting
In-Reply-To:
References:
Message-ID: <5130BEE3.9070906@gmail.com>
Dear Josep,
On 01/03/2013 15:28, Josep M Serra diaz wrote:
> Dear BIOMODers,
>
> I have been running bimod2 for my first time (great vignettes, by the way)
> and I am struggling to find the rasters/rasterStack that shows different
> ensembleForecasting according to different assembly rules (e.g. em.cv,
> em.mean, etc.). So I ended up running the script of the example to check
> whether I was confused or I did something wrong.
>
> ...and I found that after running the whole example, I cannot load either
> this rasterStack.
>
> I mean this part of the code:
>
>
> ###################################################
> ### code chunk number 19: EnsembleForecasting_loading_res
> ###################################################
> load("Myocastor/proj_t2050/Myocastor_PA1_AllRun_EM.TSS")
> Myocastor_PA1_AllRun_EM.TSS
>
>
> ###################################################
> ### code chunk number 20: EnsembleForecasting_plotting_res
> ###################################################
> plot(Myocastor_PA1_AllRun_EM.TSS)
>
>
>
> QUESTIONS:
> 1. Where can I find these different ensembles projections?
Ensemble-projections are within the object you just loaded. It's a
multidimentional object, here a rasterStack where each layer is one
ensemble model (e.g. mean, cv,...). You can know which is what with
layer names :
names(Myocastor_PA1_AllRun_EM.TSS)
> 2. The assembly rule (the way the ensemble is done; mean/median/committee)
> does not interfere with 'em.by' value in BIOMOD_EnsembleModeling()
> function. Is that correct? I mean, we can always ask to ensamble using the
> median of the predictions, or committee, but across different subsets (PA,
> PA+algo, all, etc.)
Exactly !
Cheers,
Damien
>
> Thanks,
>
> pep
> _______________________________________________
> Biomod-commits mailing list
> Biomod-commits at lists.r-forge.r-project.org
> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
From pep.bioalerts at gmail.com Fri Mar 1 16:06:46 2013
From: pep.bioalerts at gmail.com (Josep M Serra diaz)
Date: Fri, 1 Mar 2013 16:06:46 +0100
Subject: [Biomod-commits] Cannot find rasters for different rules of
ensembleForecasting
In-Reply-To: <5130BEE3.9070906@gmail.com>
References:
<5130BEE3.9070906@gmail.com>
Message-ID:
Dear Damien,
sorry I did not explain myself, ...the main thing is that this object does
not exist after running biomod2, and if I try the example step by step,
this object is not created either....
> ls()
[1] "myBiomodData" "myBiomodEF" "myBiomodEM"
"myBiomodModelEval" "myBiomodModelOut" "myBiomodOption"
"myBiomomodProj"
[8] "myBiomomodProj2050" "myCurrentProj" "myExpl"
"myExpl2050" "myResp" "myResp.ras"
"myRespName"
[15] "myRespXY"
> list.files(path='~/Myocastor/proj_t2050')
[1] "proj_t2050_ClampingMask.grd"
"proj_t2050_ClampingMask.gri"
"proj_t2050_Myocastor_PA1_Full_AllAlgos_EMbyTSS.grd"
[4] "proj_t2050_Myocastor_PA1_Full_AllAlgos_EMbyTSS.gri"
"proj_t2050_Myocastor_PA1_RUN1_AllAlgos_EMbyTSS.grd"
"proj_t2050_Myocastor_PA1_RUN1_AllAlgos_EMbyTSS.gri"
[7] "proj_t2050_Myocastor_PA2_Full_AllAlgos_EMbyTSS.grd"
"proj_t2050_Myocastor_PA2_Full_AllAlgos_EMbyTSS.gri"
"proj_t2050_Myocastor_PA2_RUN1_AllAlgos_EMbyTSS.grd"
[10] "proj_t2050_Myocastor_PA2_RUN1_AllAlgos_EMbyTSS.gri"
"proj_t2050_Myocastor_ROCbin.grd"
"proj_t2050_Myocastor_ROCbin.gri"
[13] "proj_t2050_Myocastor.grd"
"proj_t2050_Myocastor.gri"
Therefore:
load("Myocastor/proj_t2050/Myocastor_PA1_AllRun_EM.TSS")
Error in readChar(con, 5L, useBytes = TRUE) : cannot open the connection
In addition: Warning message:
In readChar(con, 5L, useBytes = TRUE) :
cannot open compressed file
'Myocastor/proj_t2050/Myocastor_PA1_AllRun_EM.TSS', probable reason 'No
such file or directory'
######### do not know if this helps
> sessionInfo()
R version 2.15.2 (2012-10-26)
Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] splines stats graphics grDevices utils datasets methods
base
other attached packages:
[1] mgcv_1.7-22 randomForest_4.6-7 mda_0.4-2
class_7.3-5 gbm_2.0-8 lattice_0.20-13 survival_2.37-2
MASS_7.3-23
[9] rpart_4.1-0 nnet_7.3-5 rgdal_0.8-5
biomod2_2.0.0 raster_2.0-41 sp_1.0-5 abind_1.4-0
loaded via a namespace (and not attached):
[1] grid_2.15.2 Matrix_1.0-11 nlme_3.1-108
2013/3/1 Damien Georges
> Dear Josep,
>
>
>
> On 01/03/2013 15:28, Josep M Serra diaz wrote:
>
>> Dear BIOMODers,
>>
>> I have been running bimod2 for my first time (great vignettes, by the way)
>> and I am struggling to find the rasters/rasterStack that shows different
>> ensembleForecasting according to different assembly rules (e.g. em.cv,
>> em.mean, etc.). So I ended up running the script of the example to check
>> whether I was confused or I did something wrong.
>>
>> ...and I found that after running the whole example, I cannot load either
>> this rasterStack.
>>
>> I mean this part of the code:
>>
>>
>> ##############################**#####################
>> ### code chunk number 19: EnsembleForecasting_loading_**res
>> ##############################**#####################
>> load("Myocastor/proj_t2050/**Myocastor_PA1_AllRun_EM.TSS")
>> Myocastor_PA1_AllRun_EM.TSS
>>
>>
>> ##############################**#####################
>> ### code chunk number 20: EnsembleForecasting_plotting_**res
>> ##############################**#####################
>> plot(Myocastor_PA1_AllRun_EM.**TSS)
>>
>>
>>
>> QUESTIONS:
>> 1. Where can I find these different ensembles projections?
>>
> Ensemble-projections are within the object you just loaded. It's a
> multidimentional object, here a rasterStack where each layer is one
> ensemble model (e.g. mean, cv,...). You can know which is what with layer
> names :
>
> names(Myocastor_PA1_AllRun_EM.**TSS)
>
>
> 2. The assembly rule (the way the ensemble is done; mean/median/committee)
>> does not interfere with 'em.by' value in BIOMOD_EnsembleModeling()
>> function. Is that correct? I mean, we can always ask to ensamble using the
>> median of the predictions, or committee, but across different subsets (PA,
>> PA+algo, all, etc.)
>>
> Exactly !
>
>
> Cheers,
>
> Damien
>
>
>> Thanks,
>>
>> pep
>> ______________________________**_________________
>> Biomod-commits mailing list
>> Biomod-commits at lists.r-forge.**r-project.org
>> https://lists.r-forge.r-**project.org/cgi-bin/mailman/**
>> listinfo/biomod-commits
>>
>
>
From postmaster at r-forge.wu-wien.ac.at Mon Mar 4 05:25:10 2013
From: postmaster at r-forge.wu-wien.ac.at (Mail Delivery Subsystem)
Date: Mon, 4 Mar 2013 11:25:10 +0700
Subject: [Biomod-commits] Status
Message-ID:
The original message was received at Mon, 4 Mar 2013 11:25:10 +0700
from r-forge.wu-wien.ac.at [80.58.191.247]
----- The following addresses had permanent fatal errors -----
biomod-commits at r-forge.wu-wien.ac.at
From damien.georges2 at gmail.com Mon Mar 4 09:52:14 2013
From: damien.georges2 at gmail.com (Damien Georges)
Date: Mon, 04 Mar 2013 09:52:14 +0100
Subject: [Biomod-commits] Cannot find rasters for different rules of
ensembleForecasting
In-Reply-To:
References:
<5130BEE3.9070906@gmail.com>
Message-ID: <513460BE.3010803@gmail.com>
Dear Josep,
I made some change in saved files names since a while to make them more
coherent within biomod2.. Moreover, if you work with raster objects,
default projections will be .grd files (raster format). So you have to
read this files with stack(...) function rather than with load(...)
Last point is if you want to make "total consensus" models (taking all
models outputs to make single model), you have to build it at
BIOMOD_EnsembleModeling(...), step setting em.by arg to 'all'.
In your case you produced enemble-models that take all algo (GLM,
RF....) for each PA dataset and each CV run.
So you have 4 different ensemble-models projections :
ef1 <- stack("proj_t2050_Myocastor_PA1_Full_AllAlgos_EMbyTSS.grd")
ef2 <- stack("proj_t2050_Myocastor_PA1_RUN1_AllAlgos_EMbyTSS.grd" )
ef3 <- stack( "proj_t2050_Myocastor_PA2_Full_AllAlgos_EMbyTSS.grd" )
ef4 <- stack("proj_t2050_Myocastor_PA2_RUN1_AllAlgos_EMbyTSS.grd")
Hope that helps,
Best,
Damien.
On 01/03/2013 16:06, Josep M Serra diaz wrote:
> Dear Damien,
>
> sorry I did not explain myself, ...the main thing is that this object
> does not exist after running biomod2, and if I try the example step by
> step, this object is not created either....
>
>
> > ls()
>
> [1] "myBiomodData" "myBiomodEF" "myBiomodEM"
> "myBiomodModelEval" "myBiomodModelOut" "myBiomodOption"
> "myBiomomodProj"
> [8] "myBiomomodProj2050" "myCurrentProj" "myExpl"
> "myExpl2050" "myResp" "myResp.ras" "myRespName"
> [15] "myRespXY"
>
>
> > list.files(path='~/Myocastor/proj_t2050')
>
> [1] "proj_t2050_ClampingMask.grd" "proj_t2050_ClampingMask.gri"
> "proj_t2050_Myocastor_PA1_Full_AllAlgos_EMbyTSS.grd"
> [4] "proj_t2050_Myocastor_PA1_Full_AllAlgos_EMbyTSS.gri"
> "proj_t2050_Myocastor_PA1_RUN1_AllAlgos_EMbyTSS.grd"
> "proj_t2050_Myocastor_PA1_RUN1_AllAlgos_EMbyTSS.gri"
> [7] "proj_t2050_Myocastor_PA2_Full_AllAlgos_EMbyTSS.grd"
> "proj_t2050_Myocastor_PA2_Full_AllAlgos_EMbyTSS.gri"
> "proj_t2050_Myocastor_PA2_RUN1_AllAlgos_EMbyTSS.grd"
> [10] "proj_t2050_Myocastor_PA2_RUN1_AllAlgos_EMbyTSS.gri"
> "proj_t2050_Myocastor_ROCbin.grd" "proj_t2050_Myocastor_ROCbin.gri"
> [13] "proj_t2050_Myocastor.grd" "proj_t2050_Myocastor.gri"
>
>
>
> Therefore:
>
> load("Myocastor/proj_t2050/Myocastor_PA1_AllRun_EM.TSS")
> Error in readChar(con, 5L, useBytes = TRUE) : cannot open the connection
> In addition: Warning message:
> In readChar(con, 5L, useBytes = TRUE) :
> cannot open compressed file
> 'Myocastor/proj_t2050/Myocastor_PA1_AllRun_EM.TSS', probable reason
> 'No such file or directory'
>
>
>
>
>
>
> ######### do not know if this helps
>
> > sessionInfo()
> R version 2.15.2 (2012-10-26)
> Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)
>
> locale:
> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
>
> attached base packages:
> [1] splines stats graphics grDevices utils datasets
> methods base
>
> other attached packages:
> [1] mgcv_1.7-22 randomForest_4.6-7 mda_0.4-2
> class_7.3-5 gbm_2.0-8 lattice_0.20-13
> survival_2.37-2 MASS_7.3-23
> [9] rpart_4.1-0 nnet_7.3-5 rgdal_0.8-5
> biomod2_2.0.0 raster_2.0-41 sp_1.0-5 abind_1.4-0
>
> loaded via a namespace (and not attached):
> [1] grid_2.15.2 Matrix_1.0-11 nlme_3.1-108
>
>
> 2013/3/1 Damien Georges >
>
> Dear Josep,
>
>
>
> On 01/03/2013 15:28, Josep M Serra diaz wrote:
>
> Dear BIOMODers,
>
> I have been running bimod2 for my first time (great vignettes,
> by the way)
> and I am struggling to find the rasters/rasterStack that shows
> different
> ensembleForecasting according to different assembly rules
> (e.g. em.cv ,
> em.mean, etc.). So I ended up running the script of the
> example to check
> whether I was confused or I did something wrong.
>
> ...and I found that after running the whole example, I cannot
> load either
> this rasterStack.
>
> I mean this part of the code:
>
>
> ###################################################
> ### code chunk number 19: EnsembleForecasting_loading_res
> ###################################################
> load("Myocastor/proj_t2050/Myocastor_PA1_AllRun_EM.TSS")
> Myocastor_PA1_AllRun_EM.TSS
>
>
> ###################################################
> ### code chunk number 20: EnsembleForecasting_plotting_res
> ###################################################
> plot(Myocastor_PA1_AllRun_EM.TSS)
>
>
>
> QUESTIONS:
> 1. Where can I find these different ensembles projections?
>
> Ensemble-projections are within the object you just loaded. It's a
> multidimentional object, here a rasterStack where each layer is
> one ensemble model (e.g. mean, cv,...). You can know which is what
> with layer names :
>
> names(Myocastor_PA1_AllRun_EM.TSS)
>
>
> 2. The assembly rule (the way the ensemble is done;
> mean/median/committee)
> does not interfere with 'em.by ' value in
> BIOMOD_EnsembleModeling()
> function. Is that correct? I mean, we can always ask to
> ensamble using the
> median of the predictions, or committee, but across different
> subsets (PA,
> PA+algo, all, etc.)
>
> Exactly !
>
>
> Cheers,
>
> Damien
>
>
> Thanks,
>
> pep
> _______________________________________________
> Biomod-commits mailing list
> Biomod-commits at lists.r-forge.r-project.org
>
> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
>
>
>
From jduquelazo at gmail.com Mon Mar 4 14:11:28 2013
From: jduquelazo at gmail.com (J DL)
Date: Mon, 4 Mar 2013 14:11:28 +0100
Subject: [Biomod-commits] Plot error.
Message-ID:
Dear all,
I am running biomod and sometimes I got this error when using the function
"plot"
plot(myExpl)
Error in as.double(y) :
cannot coerce type 'S4' to vector of type 'double'
sessionInfo()
R version 2.15.2 (2012-10-26)
Platform: x86_64-w64-mingw32/x64 (64-bit)
locale:
[1] LC_COLLATE=English_United Kingdom.1252
[2] LC_CTYPE=English_United Kingdom.1252
[3] LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.1252
attached base packages:
[1] grid splines stats graphics grDevices
[6] utils datasets methods base
other attached packages:
[1] lme4_0.999999-0 Matrix_1.0-11
[3] gdata_2.12.0 rgl_0.93.928
[5] maptools_0.8-23 car_2.0-16
[7] gclus_1.3.1 cluster_1.14.3
[9] ncdf_1.6.6 mgcv_1.7-22
[11] earth_3.2-3 plotrix_3.4-6
[13] plotmo_1.3-2 leaps_2.9
[15] nlme_3.1-108 rgdal_0.8-5
[17] foreign_0.8-52 Hmisc_3.10-1
[19] gam_1.06.2 MASS_7.3-23
[21] biomod2_2.1.7 pROC_1.5.4
[23] plyr_1.8 rpart_4.1-0
[25] randomForest_4.6-7 mda_0.4-2
[27] class_7.3-5 gbm_2.0-8
[29] lattice_0.20-13 survival_2.37-4
[31] nnet_7.3-5 raster_2.0-41
[33] sp_1.0-5 abind_1.4-0
loaded via a namespace (and not attached):
[1] gtools_2.7.0 stats4_2.15.2 tools_2.15.2
The more strange thing is that the code runs properly sometimes and others
does not.
Any clue?
Thanks in advance
From damien.georges2 at gmail.com Mon Mar 4 14:20:57 2013
From: damien.georges2 at gmail.com (Damien Georges)
Date: Mon, 04 Mar 2013 14:20:57 +0100
Subject: [Biomod-commits] Plot error.
In-Reply-To:
References:
Message-ID: <51349FB9.3060007@gmail.com>
Hi,
In the example you mentioned I guess that myExpl is a raster object..
Not a biomod2 one..
what is returned by the following lines of code?
class(myExpl)
myExpl
image(myExpl)
Is it still not working?
Best,
Damien
On 04/03/2013 14:11, J DL wrote:
> Dear all,
>
> I am running biomod and sometimes I got this error when using the function
> "plot"
>
> plot(myExpl)
> Error in as.double(y) :
> cannot coerce type 'S4' to vector of type 'double'
>
> sessionInfo()
> R version 2.15.2 (2012-10-26)
> Platform: x86_64-w64-mingw32/x64 (64-bit)
>
> locale:
> [1] LC_COLLATE=English_United Kingdom.1252
> [2] LC_CTYPE=English_United Kingdom.1252
> [3] LC_MONETARY=English_United Kingdom.1252
> [4] LC_NUMERIC=C
> [5] LC_TIME=English_United Kingdom.1252
>
> attached base packages:
> [1] grid splines stats graphics grDevices
> [6] utils datasets methods base
>
> other attached packages:
> [1] lme4_0.999999-0 Matrix_1.0-11
> [3] gdata_2.12.0 rgl_0.93.928
> [5] maptools_0.8-23 car_2.0-16
> [7] gclus_1.3.1 cluster_1.14.3
> [9] ncdf_1.6.6 mgcv_1.7-22
> [11] earth_3.2-3 plotrix_3.4-6
> [13] plotmo_1.3-2 leaps_2.9
> [15] nlme_3.1-108 rgdal_0.8-5
> [17] foreign_0.8-52 Hmisc_3.10-1
> [19] gam_1.06.2 MASS_7.3-23
> [21] biomod2_2.1.7 pROC_1.5.4
> [23] plyr_1.8 rpart_4.1-0
> [25] randomForest_4.6-7 mda_0.4-2
> [27] class_7.3-5 gbm_2.0-8
> [29] lattice_0.20-13 survival_2.37-4
> [31] nnet_7.3-5 raster_2.0-41
> [33] sp_1.0-5 abind_1.4-0
>
> loaded via a namespace (and not attached):
> [1] gtools_2.7.0 stats4_2.15.2 tools_2.15.2
>
> The more strange thing is that the code runs properly sometimes and others
> does not.
>
> Any clue?
>
> Thanks in advance
> _______________________________________________
> Biomod-commits mailing list
> Biomod-commits at lists.r-forge.r-project.org
> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
From postmaster at r-forge.wu-wien.ac.at Wed Mar 6 04:31:54 2013
From: postmaster at r-forge.wu-wien.ac.at (Automatic Email Delivery Software)
Date: Wed, 6 Mar 2013 10:31:54 +0700
Subject: [Biomod-commits] DELIVERY FAILED
Message-ID:
The original message was received at Wed, 6 Mar 2013 10:31:54 +0700
from 207.0.156.66
----- The following addresses had permanent fatal errors -----
biomod-commits at r-forge.wu-wien.ac.at
From pep.bioalerts at gmail.com Sun Mar 10 20:20:10 2013
From: pep.bioalerts at gmail.com (Josep M Serra diaz)
Date: Sun, 10 Mar 2013 20:20:10 +0100
Subject: [Biomod-commits] Error in ensemble by algorithm
Message-ID:
Dear BIOMODers,
I found an error while trying to perform modeling ensemble by algorithm in
order to produce an output for each statistical technique
Any clue of what does this mean???
The strane
########################
#ensemble through algorithm
myBiomodEM.algo <- BIOMOD_EnsembleModeling (
em.by="algo" ,
modeling.output = myBiomodModelOut,
chosen.models = 'all',
eval.metric = 'TSS',
eval.metric.quality.threshold =
c(0.6),
prob.mean=T,
prob.cv = T,
prob.ci = T,
prob.ci.alpha = 0.05,
prob.median = T,
committee.averaging = T,
prob.mean.weight = F,
prob.mean.weight.decay =
'proportional'
)
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
Build Ensemble Models
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
! all models available will be included in ensemble.modeling
> Evaluation & Weighting methods summary :
TSS over 0.6
> GLM_AllRun ensemble modeling
> TSS
! No models kept due to treshold filtering... Ensemble Modeling was skip!
> GBM_AllRun ensemble modeling
> TSS
! No models kept due to treshold filtering... Ensemble Modeling was skip!
> GAM_AllRun ensemble modeling
> TSS
! No models kept due to treshold filtering... Ensemble Modeling was skip!
> CTA_AllRun ensemble modeling
> TSS
! No models kept due to treshold filtering... Ensemble Modeling was skip!
> ANN_AllRun ensemble modeling
> TSS
! No models kept due to treshold filtering... Ensemble Modeling was skip!
> FDA_AllRun ensemble modeling
> TSS
! No models kept due to treshold filtering... Ensemble Modeling was skip!
> MARS_AllRun ensemble modeling
> TSS
! No models kept due to treshold filtering... Ensemble Modeling was skip!
> RF_AllRun ensemble modeling
> TSS
> models kept : Quercusilex_AllData_RUN2_RF
! Models projections for whole zonation required...
> Projecting Quercusilex_AllData_RUN2_RF ...
> Mean of probabilities...
> Coef of variation of probabilities...
> Median of ptobabilities...
> Confidence Interval...
> 2.5 %
> 97.5 %
> Comittee averaging...Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR",
"SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
*Error in roc.default(Obs, Fit, percent = T) : No control observation.*
In addition: There were 50 or more warnings (use warnings() to see the
first 50)
> warnings()
Warning messages:
1: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
NAs produced by integer overflow
2: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
NAs produced by integer overflow
3: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
NAs produced by integer overflow
4: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
NAs produced by integer overflow
5: In forecast_0 * observed_0 : NAs produced by integer overflow
6: In forecast_0 * observed_0 : NAs produced by integer overflow
7: In forecast_0 * observed_0 : NAs produced by integer overflow
8: In forecast_0 * observed_0 : NAs produced by integer overflow
9: In forecast_0 * observed_0 : NAs produced by integer overflow
10: In forecast_0 * observed_0 : NAs produced by integer overflow
11: In forecast_0 * observed_0 : NAs produced by integer overflow
12: In forecast_0 * observed_0 : NAs produced by integer overflow
13: In forecast_0 * observed_0 : NAs produced by integer overflow
14: In forecast_0 * observed_0 : NAs produced by integer overflow
15: In forecast_0 * observed_0 : NAs produced by integer overflow
16: In forecast_0 * observed_0 : NAs produced by integer overflow
17: In forecast_0 * observed_0 : NAs produced by integer overflow
18: In forecast_0 * observed_0 : NAs produced by integer overflow
19: In forecast_0 * observed_0 : NAs produced by integer overflow
20: In forecast_0 * observed_0 : NAs produced by integer overflow
21: In forecast_0 * observed_0 : NAs produced by integer overflow
22: In forecast_0 * observed_0 : NAs produced by integer overflow
23: In forecast_0 * observed_0 : NAs produced by integer overflow
24: In forecast_0 * observed_0 : NAs produced by integer overflow
25: In forecast_0 * observed_0 : NAs produced by integer overflow
26: In forecast_0 * observed_0 : NAs produced by integer overflow
27: In forecast_0 * observed_0 : NAs produced by integer overflow
28: In forecast_0 * observed_0 : NAs produced by integer overflow
29: In forecast_0 * observed_0 : NAs produced by integer overflow
30: In forecast_0 * observed_0 : NAs produced by integer overflow
31: In forecast_0 * observed_0 : NAs produced by integer overflow
32: In forecast_0 * observed_0 : NAs produced by integer overflow
33: In forecast_0 * observed_0 : NAs produced by integer overflow
34: In forecast_0 * observed_0 : NAs produced by integer overflow
35: In forecast_0 * observed_0 : NAs produced by integer overflow
36: In forecast_0 * observed_0 : NAs produced by integer overflow
37: In forecast_0 * observed_0 : NAs produced by integer overflow
38: In forecast_0 * observed_0 : NAs produced by integer overflow
39: In forecast_0 * observed_0 : NAs produced by integer overflow
40: In forecast_0 * observed_0 : NAs produced by integer overflow
41: In forecast_0 * observed_0 : NAs produced by integer overflow
42: In forecast_0 * observed_0 : NAs produced by integer overflow
43: In forecast_0 * observed_0 : NAs produced by integer overflow
44: In forecast_0 * observed_0 : NAs produced by integer overflow
45: In forecast_0 * observed_0 : NAs produced by integer overflow
46: In forecast_0 * observed_0 : NAs produced by integer overflow
47: In forecast_0 * observed_0 : NAs produced by integer overflow
48: In forecast_0 * observed_0 : NAs produced by integer overflow
49: In forecast_0 * observed_0 : NAs produced by integer overflow
50: In forecast_0 * observed_0 : NAs produced by integer overflow
From wilfried.thuiller at ujf-grenoble.fr Mon Mar 11 07:15:59 2013
From: wilfried.thuiller at ujf-grenoble.fr (Wilfried Thuiller)
Date: Mon, 11 Mar 2013 07:15:59 +0100
Subject: [Biomod-commits] Error in ensemble by algorithm
In-Reply-To:
References:
Message-ID: <08165BA5-5076-43E4-890E-348A0E9DD2B6@ujf-grenoble.fr>
Dear Josep,
Please make sure to use the latest version (2.1.13). From the what is pasted below, it seems that the minimum threshold to select the models for the ensemble forecast is too high and no models are selected.
Try to put 0.4 for instance and run the script again.
Best regards,
Wilfried
Le 10 mars 2013 ? 20:20, Josep M Serra diaz a ?crit :
> Dear BIOMODers,
>
> I found an error while trying to perform modeling ensemble by algorithm in
> order to produce an output for each statistical technique
>
>
> Any clue of what does this mean???
>
> The strane
>
>
> ########################
>
> #ensemble through algorithm
> myBiomodEM.algo <- BIOMOD_EnsembleModeling (
> em.by="algo" ,
> modeling.output = myBiomodModelOut,
> chosen.models = 'all',
> eval.metric = 'TSS',
> eval.metric.quality.threshold =
> c(0.6),
> prob.mean=T,
> prob.cv = T,
> prob.ci = T,
> prob.ci.alpha = 0.05,
> prob.median = T,
> committee.averaging = T,
> prob.mean.weight = F,
> prob.mean.weight.decay =
> 'proportional'
> )
>
>
> -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
> Build Ensemble Models
> -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
>
> ! all models available will be included in ensemble.modeling
>> Evaluation & Weighting methods summary :
> TSS over 0.6
>
>
>> GLM_AllRun ensemble modeling
>> TSS
> ! No models kept due to treshold filtering... Ensemble Modeling was skip!
>
>> GBM_AllRun ensemble modeling
>> TSS
> ! No models kept due to treshold filtering... Ensemble Modeling was skip!
>
>> GAM_AllRun ensemble modeling
>> TSS
> ! No models kept due to treshold filtering... Ensemble Modeling was skip!
>
>> CTA_AllRun ensemble modeling
>> TSS
> ! No models kept due to treshold filtering... Ensemble Modeling was skip!
>
>> ANN_AllRun ensemble modeling
>> TSS
> ! No models kept due to treshold filtering... Ensemble Modeling was skip!
>
>> FDA_AllRun ensemble modeling
>> TSS
> ! No models kept due to treshold filtering... Ensemble Modeling was skip!
>
>> MARS_AllRun ensemble modeling
>> TSS
> ! No models kept due to treshold filtering... Ensemble Modeling was skip!
>
>> RF_AllRun ensemble modeling
>> TSS
>> models kept : Quercusilex_AllData_RUN2_RF
> ! Models projections for whole zonation required...
>> Projecting Quercusilex_AllData_RUN2_RF ...
>
>> Mean of probabilities...
>> Coef of variation of probabilities...
>> Median of ptobabilities...
>> Confidence Interval...
>> 2.5 %
>> 97.5 %
>> Comittee averaging...Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR",
> "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
> *Error in roc.default(Obs, Fit, percent = T) : No control observation.*
> In addition: There were 50 or more warnings (use warnings() to see the
> first 50)
>> warnings()
> Warning messages:
> 1: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
> NAs produced by integer overflow
> 2: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
> NAs produced by integer overflow
> 3: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
> NAs produced by integer overflow
> 4: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
> NAs produced by integer overflow
> 5: In forecast_0 * observed_0 : NAs produced by integer overflow
> 6: In forecast_0 * observed_0 : NAs produced by integer overflow
> 7: In forecast_0 * observed_0 : NAs produced by integer overflow
> 8: In forecast_0 * observed_0 : NAs produced by integer overflow
> 9: In forecast_0 * observed_0 : NAs produced by integer overflow
> 10: In forecast_0 * observed_0 : NAs produced by integer overflow
> 11: In forecast_0 * observed_0 : NAs produced by integer overflow
> 12: In forecast_0 * observed_0 : NAs produced by integer overflow
> 13: In forecast_0 * observed_0 : NAs produced by integer overflow
> 14: In forecast_0 * observed_0 : NAs produced by integer overflow
> 15: In forecast_0 * observed_0 : NAs produced by integer overflow
> 16: In forecast_0 * observed_0 : NAs produced by integer overflow
> 17: In forecast_0 * observed_0 : NAs produced by integer overflow
> 18: In forecast_0 * observed_0 : NAs produced by integer overflow
> 19: In forecast_0 * observed_0 : NAs produced by integer overflow
> 20: In forecast_0 * observed_0 : NAs produced by integer overflow
> 21: In forecast_0 * observed_0 : NAs produced by integer overflow
> 22: In forecast_0 * observed_0 : NAs produced by integer overflow
> 23: In forecast_0 * observed_0 : NAs produced by integer overflow
> 24: In forecast_0 * observed_0 : NAs produced by integer overflow
> 25: In forecast_0 * observed_0 : NAs produced by integer overflow
> 26: In forecast_0 * observed_0 : NAs produced by integer overflow
> 27: In forecast_0 * observed_0 : NAs produced by integer overflow
> 28: In forecast_0 * observed_0 : NAs produced by integer overflow
> 29: In forecast_0 * observed_0 : NAs produced by integer overflow
> 30: In forecast_0 * observed_0 : NAs produced by integer overflow
> 31: In forecast_0 * observed_0 : NAs produced by integer overflow
> 32: In forecast_0 * observed_0 : NAs produced by integer overflow
> 33: In forecast_0 * observed_0 : NAs produced by integer overflow
> 34: In forecast_0 * observed_0 : NAs produced by integer overflow
> 35: In forecast_0 * observed_0 : NAs produced by integer overflow
> 36: In forecast_0 * observed_0 : NAs produced by integer overflow
> 37: In forecast_0 * observed_0 : NAs produced by integer overflow
> 38: In forecast_0 * observed_0 : NAs produced by integer overflow
> 39: In forecast_0 * observed_0 : NAs produced by integer overflow
> 40: In forecast_0 * observed_0 : NAs produced by integer overflow
> 41: In forecast_0 * observed_0 : NAs produced by integer overflow
> 42: In forecast_0 * observed_0 : NAs produced by integer overflow
> 43: In forecast_0 * observed_0 : NAs produced by integer overflow
> 44: In forecast_0 * observed_0 : NAs produced by integer overflow
> 45: In forecast_0 * observed_0 : NAs produced by integer overflow
> 46: In forecast_0 * observed_0 : NAs produced by integer overflow
> 47: In forecast_0 * observed_0 : NAs produced by integer overflow
> 48: In forecast_0 * observed_0 : NAs produced by integer overflow
> 49: In forecast_0 * observed_0 : NAs produced by integer overflow
> 50: In forecast_0 * observed_0 : NAs produced by integer overflow
> _______________________________________________
> Biomod-commits mailing list
> Biomod-commits at lists.r-forge.r-project.org
> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
--------------------------
Dr. Wilfried Thuiller
Laboratoire d'Ecologie Alpine, UMR CNRS 5553
Universit? Joseph Fourier
BP53, 38041 Grenoble cedex 9, France
tel: +33 (0)4 76 51 44 97
fax: +33 (0)4 76 51 42 79
Email: wilfried.thuiller at ujf-grenoble.fr
Personal website: http://www.will.chez-alice.fr
Team website: http://www-leca.ujf-grenoble.fr/equipes/emabio.htm
ERC Starting Grant TEEMBIO project: http://www.will.chez-alice.fr/Research.html
FP6 European EcoChange project: http://www.ecochange-project.eu
From postmaster at r-forge.wu-wien.ac.at Tue Mar 12 06:55:58 2013
From: postmaster at r-forge.wu-wien.ac.at (Post Office)
Date: Tue, 12 Mar 2013 12:55:58 +0700
Subject: [Biomod-commits] DELIVERY FAILED
Message-ID:
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References:
<08165BA5-5076-43E4-890E-348A0E9DD2B6@ujf-grenoble.fr>
Message-ID:
Thanx foru your answers, it did work when lowering the quality threshold...
However, it seems that this error came up when the quality threshold only
selects one model (the case of Random forests), than it breaks the
calculation because it cannot be ensembled. It is like biomod2 is prepared
to say ' no models selected' but if 1 model is selected then the error
appears.
It is my impression but I have no clue,
Best,
Pep
2013/3/11 Wilfried Thuiller
> Dear Josep,
>
> Please make sure to use the latest version (2.1.13). From the what is
> pasted below, it seems that the minimum threshold to select the models for
> the ensemble forecast is too high and no models are selected.
> Try to put 0.4 for instance and run the script again.
>
> Best regards,
> Wilfried
>
>
>
> Le 10 mars 2013 ? 20:20, Josep M Serra diaz a ?crit :
>
> Dear BIOMODers,
>
> I found an error while trying to perform modeling ensemble by algorithm in
> order to produce an output for each statistical technique
>
>
> Any clue of what does this mean???
>
> The strane
>
>
> ########################
>
> #ensemble through algorithm
> myBiomodEM.algo <- BIOMOD_EnsembleModeling (
> em.by="algo" ,
> modeling.output = myBiomodModelOut,
> chosen.models = 'all',
> eval.metric = 'TSS',
> eval.metric.quality.threshold =
> c(0.6),
> prob.mean=T,
> prob.cv = T,
> prob.ci = T,
> prob.ci.alpha = 0.05,
> prob.median = T,
> committee.averaging = T,
> prob.mean.weight = F,
> prob.mean.weight.decay =
> 'proportional'
> )
>
>
> -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
> Build Ensemble Models
> -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
>
> ! all models available will be included in ensemble.modeling
>
> Evaluation & Weighting methods summary :
>
> TSS over 0.6
>
>
> GLM_AllRun ensemble modeling
>
> TSS
>
> ! No models kept due to treshold filtering... Ensemble Modeling was skip!
>
> GBM_AllRun ensemble modeling
>
> TSS
>
> ! No models kept due to treshold filtering... Ensemble Modeling was skip!
>
> GAM_AllRun ensemble modeling
>
> TSS
>
> ! No models kept due to treshold filtering... Ensemble Modeling was skip!
>
> CTA_AllRun ensemble modeling
>
> TSS
>
> ! No models kept due to treshold filtering... Ensemble Modeling was skip!
>
> ANN_AllRun ensemble modeling
>
> TSS
>
> ! No models kept due to treshold filtering... Ensemble Modeling was skip!
>
> FDA_AllRun ensemble modeling
>
> TSS
>
> ! No models kept due to treshold filtering... Ensemble Modeling was skip!
>
> MARS_AllRun ensemble modeling
>
> TSS
>
> ! No models kept due to treshold filtering... Ensemble Modeling was skip!
>
> RF_AllRun ensemble modeling
>
> TSS
>
> models kept : Quercusilex_AllData_RUN2_RF
>
> ! Models projections for whole zonation required...
>
> Projecting Quercusilex_AllData_RUN2_RF ...
>
>
> Mean of probabilities...
>
> Coef of variation of probabilities...
>
> Median of ptobabilities...
>
> Confidence Interval...
>
> 2.5 %
>
> 97.5 %
>
> Comittee averaging...Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR",
>
> "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
> *Error in roc.default(Obs, Fit, percent = T) : No control observation.*
>
> In addition: There were 50 or more warnings (use warnings() to see the
> first 50)
>
> warnings()
>
> Warning messages:
> 1: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
> NAs produced by integer overflow
> 2: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
> NAs produced by integer overflow
> 3: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
> NAs produced by integer overflow
> 4: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
> NAs produced by integer overflow
> 5: In forecast_0 * observed_0 : NAs produced by integer overflow
> 6: In forecast_0 * observed_0 : NAs produced by integer overflow
> 7: In forecast_0 * observed_0 : NAs produced by integer overflow
> 8: In forecast_0 * observed_0 : NAs produced by integer overflow
> 9: In forecast_0 * observed_0 : NAs produced by integer overflow
> 10: In forecast_0 * observed_0 : NAs produced by integer overflow
> 11: In forecast_0 * observed_0 : NAs produced by integer overflow
> 12: In forecast_0 * observed_0 : NAs produced by integer overflow
> 13: In forecast_0 * observed_0 : NAs produced by integer overflow
> 14: In forecast_0 * observed_0 : NAs produced by integer overflow
> 15: In forecast_0 * observed_0 : NAs produced by integer overflow
> 16: In forecast_0 * observed_0 : NAs produced by integer overflow
> 17: In forecast_0 * observed_0 : NAs produced by integer overflow
> 18: In forecast_0 * observed_0 : NAs produced by integer overflow
> 19: In forecast_0 * observed_0 : NAs produced by integer overflow
> 20: In forecast_0 * observed_0 : NAs produced by integer overflow
> 21: In forecast_0 * observed_0 : NAs produced by integer overflow
> 22: In forecast_0 * observed_0 : NAs produced by integer overflow
> 23: In forecast_0 * observed_0 : NAs produced by integer overflow
> 24: In forecast_0 * observed_0 : NAs produced by integer overflow
> 25: In forecast_0 * observed_0 : NAs produced by integer overflow
> 26: In forecast_0 * observed_0 : NAs produced by integer overflow
> 27: In forecast_0 * observed_0 : NAs produced by integer overflow
> 28: In forecast_0 * observed_0 : NAs produced by integer overflow
> 29: In forecast_0 * observed_0 : NAs produced by integer overflow
> 30: In forecast_0 * observed_0 : NAs produced by integer overflow
> 31: In forecast_0 * observed_0 : NAs produced by integer overflow
> 32: In forecast_0 * observed_0 : NAs produced by integer overflow
> 33: In forecast_0 * observed_0 : NAs produced by integer overflow
> 34: In forecast_0 * observed_0 : NAs produced by integer overflow
> 35: In forecast_0 * observed_0 : NAs produced by integer overflow
> 36: In forecast_0 * observed_0 : NAs produced by integer overflow
> 37: In forecast_0 * observed_0 : NAs produced by integer overflow
> 38: In forecast_0 * observed_0 : NAs produced by integer overflow
> 39: In forecast_0 * observed_0 : NAs produced by integer overflow
> 40: In forecast_0 * observed_0 : NAs produced by integer overflow
> 41: In forecast_0 * observed_0 : NAs produced by integer overflow
> 42: In forecast_0 * observed_0 : NAs produced by integer overflow
> 43: In forecast_0 * observed_0 : NAs produced by integer overflow
> 44: In forecast_0 * observed_0 : NAs produced by integer overflow
> 45: In forecast_0 * observed_0 : NAs produced by integer overflow
> 46: In forecast_0 * observed_0 : NAs produced by integer overflow
> 47: In forecast_0 * observed_0 : NAs produced by integer overflow
> 48: In forecast_0 * observed_0 : NAs produced by integer overflow
> 49: In forecast_0 * observed_0 : NAs produced by integer overflow
> 50: In forecast_0 * observed_0 : NAs produced by integer overflow
> _______________________________________________
> Biomod-commits mailing list
> Biomod-commits at lists.r-forge.r-project.org
> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
>
>
> --------------------------
> Dr. Wilfried Thuiller
> Laboratoire d'Ecologie Alpine, UMR CNRS 5553
> Universit? Joseph Fourier
> BP53, 38041 Grenoble cedex 9, France
> tel: +33 (0)4 76 51 44 97
> fax: +33 (0)4 76 51 42 79
>
> Email: wilfried.thuiller at ujf-grenoble.fr
> Personal website: http://www.will.chez-alice.fr
> Team website: http://www-leca.ujf-grenoble.fr/equipes/emabio.htm
>
> ERC Starting Grant TEEMBIO project:
> http://www.will.chez-alice.fr/Research.html
> FP6 European EcoChange project: http://www.ecochange-project.eu
>
>
>
>
>
>
>
>
From kpitts at siu.edu Tue Mar 12 20:15:17 2013
From: kpitts at siu.edu (Kristen Bouska)
Date: Tue, 12 Mar 2013 14:15:17 -0500
Subject: [Biomod-commits] (no subject)
Message-ID:
library(biomod2)
train_pa <- read.csv(system.file("external/species/Train_PA.csv",
package="biomod2"))
myRespName <- 'PTM'
myResp <- as.numeric(train_pa[,myRespName])
test_pa <- read.csv(system.file("external/species/Test_PA.csv",
package="biomod2"))
myRespEval <- 'PTM'
myEvalpa <- as.numeric(test_pa[,myRespName])
train_env <-read.csv(system.file("external/bioclim/current/train_env.csv",
package="biomod2"))
myExpl <- as.data.frame(train_env)
test_env <-read.csv(system.file("external/bioclim/current/test_env.csv",
package="biomod2"))
myEvalenv <- as.data.frame(test_env)
myBiomodData <- BIOMOD_FormatingData(resp.var=myResp, expl.var=myExpl,
resp.name=myRespName, eval.resp.var=myEvalpa, eval.expl.var=myEvalenv)
> No pseudo absences selection !Error in validObject(.Object) :
invalid class ?BIOMOD.formated.data? object: invalid object for slot
"coord" in class "BIOMOD.formated.data": got class "NULL", should be or
extend class "data.frame"
--
Kristen Bouska
Environmental Resources and Policy Program
Life Sciences II, Room 375 (Mail Code 4325)
Southern Illinois University - Carbondale
Carbondale, IL 62901
618-924-2592
From pep.bioalerts at gmail.com Tue Mar 12 20:16:54 2013
From: pep.bioalerts at gmail.com (Josep M Serra diaz)
Date: Tue, 12 Mar 2013 20:16:54 +0100
Subject: [Biomod-commits] Error in ensemble by algorithm
In-Reply-To:
References:
<08165BA5-5076-43E4-890E-348A0E9DD2B6@ujf-grenoble.fr>
Message-ID:
Wilfried and colleagues,
This error in ensembling by algorithm comes up again, even though I am not
selecting for a high quality threshold.
I am using last version of R and biomod2
question 1: Why does this warning appear appear?
*
"Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions "*
question 2: the real error that breaks the ensembling is this one. Why is
that?
*Error in roc.default(Obs, Fit, percent = T) : No control observation.*
Thanks a lot for your time and effort,
Pep
Find history hereunder:
myBiomodEM.algo <- BIOMOD_EnsembleModeling (
+ em.by="algo" ,
+ modeling.output =
myBiomodModelOut,
+ chosen.models = 'all',
+ eval.metric = 'TSS',
+ eval.metric.quality.threshold =
c(0.0), # we want them all
+ prob.mean=T,
+ prob.cv = T,
+ prob.ci = T,
+ prob.ci.alpha = 0.05,
+ prob.median = T,
+ committee.averaging = T,
+ prob.mean.weight = F,
+ prob.mean.weight.decay =
'proportional'
+ )
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Build
Ensemble Models
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
! all models available will be included in ensemble.modeling
> Evaluation & Weighting methods summary :
TSS over 0
> GLM_AllRun ensemble modeling
> TSS
> models kept : Quercushumilis_AllData_RUN1_GLM,
Quercushumilis_AllData_RUN2_GLM, Quercushumilis_AllData_RUN3_GLM
! Models projections for whole zonation required...
> Projecting Quercushumilis_AllData_RUN1_GLM ...
> Projecting Quercushumilis_AllData_RUN2_GLM ...
> Projecting Quercushumilis_AllData_RUN3_GLM ...
> Mean of probabilities...
> Coef of variation of probabilities...
> Median of ptobabilities...
> Confidence Interval...
> 2.5 %
> 97.5 %
> Comittee averaging...
> GBM_AllRun ensemble modeling
> TSS
> models kept : Quercushumilis_AllData_RUN1_GBM,
Quercushumilis_AllData_RUN2_GBM, Quercushumilis_AllData_RUN3_GBM
! Models projections for whole zonation required...
> Projecting Quercushumilis_AllData_RUN1_GBM ...
> Projecting Quercushumilis_AllData_RUN2_GBM ...
> Projecting Quercushumilis_AllData_RUN3_GBM ...
> Mean of probabilities...
> Coef of variation of probabilities...
> Median of ptobabilities...
> Confidence Interval...
> 2.5 %
> 97.5 %
> Comittee averaging...
> GAM_AllRun ensemble modeling
> TSS
> models kept : Quercushumilis_AllData_RUN1_GAM,
Quercushumilis_AllData_RUN2_GAM, Quercushumilis_AllData_RUN3_GAM
! Models projections for whole zonation required...
> Projecting Quercushumilis_AllData_RUN1_GAM ...
> Projecting Quercushumilis_AllData_RUN2_GAM ...
> Projecting Quercushumilis_AllData_RUN3_GAM ...
> Mean of probabilities...
> Coef of variation of probabilities...
> Median of ptobabilities...
> Confidence Interval...
> 2.5 %
> 97.5 %
> Comittee averaging...Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR",
"SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
> CTA_AllRun ensemble modeling
> TSS
> models kept : Quercushumilis_AllData_RUN1_CTA,
Quercushumilis_AllData_RUN2_CTA, Quercushumilis_AllData_RUN3_CTA
! Models projections for whole zonation required...
> Projecting Quercushumilis_AllData_RUN1_CTA ...
> Projecting Quercushumilis_AllData_RUN2_CTA ...
> Projecting Quercushumilis_AllData_RUN3_CTA ...
> Mean of probabilities...
> Coef of variation of probabilities...
> Median of ptobabilities...
> Confidence Interval...
> 2.5 %
> 97.5 %
> Comittee averaging...
> ANN_AllRun ensemble modeling
> TSS
> models kept : Quercushumilis_AllData_RUN3_ANN
! Models projections for whole zonation required...
> Projecting Quercushumilis_AllData_RUN3_ANN ...
> Mean of probabilities...
> Coef of variation of probabilities...
> Median of ptobabilities...
> Confidence Interval...
> 2.5 %
> 97.5 %
> Comittee averaging...Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR",
"SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Error in roc.default(Obs, Fit, percent = T) : No control observation.
In addition: There were 50 or more warnings (use warnings() to see the
first 50)
>
> 2013/3/11 Wilfried Thuiller
>
>> Dear Josep,
>>
>> Please make sure to use the latest version (2.1.13). From the what is
>> pasted below, it seems that the minimum threshold to select the models for
>> the ensemble forecast is too high and no models are selected.
>> Try to put 0.4 for instance and run the script again.
>>
>> Best regards,
>> Wilfried
>>
>>
>>
>> Le 10 mars 2013 ? 20:20, Josep M Serra diaz a ?crit :
>>
>> Dear BIOMODers,
>>
>> I found an error while trying to perform modeling ensemble by algorithm in
>> order to produce an output for each statistical technique
>>
>>
>> Any clue of what does this mean???
>>
>> The strane
>>
>>
>> ########################
>>
>> #ensemble through algorithm
>> myBiomodEM.algo <- BIOMOD_EnsembleModeling (
>> em.by="algo" ,
>> modeling.output = myBiomodModelOut,
>> chosen.models = 'all',
>> eval.metric = 'TSS',
>> eval.metric.quality.threshold =
>> c(0.6),
>> prob.mean=T,
>> prob.cv = T,
>> prob.ci = T,
>> prob.ci.alpha = 0.05,
>> prob.median = T,
>> committee.averaging = T,
>> prob.mean.weight = F,
>> prob.mean.weight.decay =
>> 'proportional'
>> )
>>
>>
>> -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
>> Build Ensemble Models
>> -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
>>
>> ! all models available will be included in ensemble.modeling
>>
>> Evaluation & Weighting methods summary :
>>
>> TSS over 0.6
>>
>>
>> GLM_AllRun ensemble modeling
>>
>> TSS
>>
>> ! No models kept due to treshold filtering... Ensemble Modeling was
>> skip!
>>
>> GBM_AllRun ensemble modeling
>>
>> TSS
>>
>> ! No models kept due to treshold filtering... Ensemble Modeling was
>> skip!
>>
>> GAM_AllRun ensemble modeling
>>
>> TSS
>>
>> ! No models kept due to treshold filtering... Ensemble Modeling was
>> skip!
>>
>> CTA_AllRun ensemble modeling
>>
>> TSS
>>
>> ! No models kept due to treshold filtering... Ensemble Modeling was
>> skip!
>>
>> ANN_AllRun ensemble modeling
>>
>> TSS
>>
>> ! No models kept due to treshold filtering... Ensemble Modeling was
>> skip!
>>
>> FDA_AllRun ensemble modeling
>>
>> TSS
>>
>> ! No models kept due to treshold filtering... Ensemble Modeling was
>> skip!
>>
>> MARS_AllRun ensemble modeling
>>
>> TSS
>>
>> ! No models kept due to treshold filtering... Ensemble Modeling was
>> skip!
>>
>> RF_AllRun ensemble modeling
>>
>> TSS
>>
>> models kept : Quercusilex_AllData_RUN2_RF
>>
>> ! Models projections for whole zonation required...
>>
>> Projecting Quercusilex_AllData_RUN2_RF ...
>>
>>
>> Mean of probabilities...
>>
>> Coef of variation of probabilities...
>>
>> Median of ptobabilities...
>>
>> Confidence Interval...
>>
>> 2.5 %
>>
>> 97.5 %
>>
>> Comittee averaging...Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR",
>>
>> "SR", "ACCURACY", "BIAS", :
>>
>> Observed or fited data contains a unique value.. Be carefull with this
>> models predictions
>>
>> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS",
>> :
>>
>> Observed or fited data contains a unique value.. Be carefull with this
>> models predictions
>>
>> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS",
>> :
>>
>> Observed or fited data contains a unique value.. Be carefull with this
>> models predictions
>>
>> *Error in roc.default(Obs, Fit, percent = T) : No control observation.*
>>
>> In addition: There were 50 or more warnings (use warnings() to see the
>> first 50)
>>
>> warnings()
>>
>> Warning messages:
>> 1: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
>> NAs produced by integer overflow
>> 2: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
>> NAs produced by integer overflow
>> 3: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
>> NAs produced by integer overflow
>> 4: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
>> NAs produced by integer overflow
>> 5: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 6: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 7: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 8: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 9: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 10: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 11: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 12: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 13: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 14: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 15: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 16: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 17: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 18: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 19: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 20: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 21: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 22: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 23: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 24: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 25: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 26: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 27: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 28: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 29: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 30: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 31: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 32: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 33: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 34: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 35: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 36: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 37: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 38: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 39: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 40: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 41: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 42: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 43: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 44: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 45: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 46: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 47: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 48: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 49: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 50: In forecast_0 * observed_0 : NAs produced by integer overflow
>> _______________________________________________
>> Biomod-commits mailing list
>> Biomod-commits at lists.r-forge.r-project.org
>>
>> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
>>
>>
>> --------------------------
>> Dr. Wilfried Thuiller
>> Laboratoire d'Ecologie Alpine, UMR CNRS 5553
>> Universit? Joseph Fourier
>> BP53, 38041 Grenoble cedex 9, France
>> tel: +33 (0)4 76 51 44 97
>> fax: +33 (0)4 76 51 42 79
>>
>> Email: wilfried.thuiller at ujf-grenoble.fr
>> Personal website: http://www.will.chez-alice.fr
>> Team website: http://www-leca.ujf-grenoble.fr/equipes/emabio.htm
>>
>> ERC Starting Grant TEEMBIO project:
>> http://www.will.chez-alice.fr/Research.html
>> FP6 European EcoChange project: http://www.ecochange-project.eu
>>
>>
>>
>>
>>
>>
>>
>>
>
From kpitts at siu.edu Tue Mar 12 20:22:51 2013
From: kpitts at siu.edu (Kristen Bouska)
Date: Tue, 12 Mar 2013 14:22:51 -0500
Subject: [Biomod-commits] (no subject)
In-Reply-To:
References:
Message-ID:
Apologies for accidentally sending the previous message without my
question. When formatting my data, I keep receiving the following error *"No
pseudo absences selection !Error in validObject(.Object) :
invalid class ?BIOMOD.formated.data? object: invalid object for slot
"coord" in class "BIOMOD.formated.data": got class "NULL", should be or
extend class "data.frame"* and am unsure of what this means or how to solve
this problem. When I check the data, it appears to be set up how I'd like
it, but perhaps I have my code messed up? Please bear with me, I am a
beginner with both Biomod2 and R.
library(biomod2)
train_pa <- read.csv(system.file("
external/species/Train_PA.csv",
package="biomod2"))
myRespName <- 'PTM'
myResp <- as.numeric(train_pa[,myRespName])
test_pa <- read.csv(system.file("external/species/Test_PA.csv",
package="biomod2"))
myRespEval <- 'PTM'
myEvalpa <- as.numeric(test_pa[,myRespName])
train_env <-read.csv(system.file("external/bioclim/current/train_env.csv",
package="biomod2"))
myExpl <- as.data.frame(train_env)
test_env <-read.csv(system.file("external/bioclim/current/test_env.csv",
package="biomod2"))
myEvalenv <- as.data.frame(test_env)
myBiomodData <- BIOMOD_FormatingData(resp.var=myResp, expl.var=myExpl,
resp.name=myRespName, eval.resp.var=myEvalpa, eval.expl.var=myEvalenv)
On Tue, Mar 12, 2013 at 2:15 PM, Kristen Bouska wrote:
> library(biomod2)
> train_pa <- read.csv(system.file("external/species/Train_PA.csv",
> package="biomod2"))
> myRespName <- 'PTM'
> myResp <- as.numeric(train_pa[,myRespName])
> test_pa <- read.csv(system.file("external/species/Test_PA.csv",
> package="biomod2"))
> myRespEval <- 'PTM'
> myEvalpa <- as.numeric(test_pa[,myRespName])
> train_env <-read.csv(system.file("external/bioclim/current/train_env.csv",
> package="biomod2"))
> myExpl <- as.data.frame(train_env)
> test_env <-read.csv(system.file("external/bioclim/current/test_env.csv",
> package="biomod2"))
> myEvalenv <- as.data.frame(test_env)
> myBiomodData <- BIOMOD_FormatingData(resp.var=myResp, expl.var=myExpl,
> resp.name=myRespName, eval.resp.var=myEvalpa, eval.expl.var=myEvalenv)
>
> > No pseudo absences selection !Error in validObject(.Object) :
> invalid class ?BIOMOD.formated.data? object: invalid object for slot
> "coord" in class "BIOMOD.formated.data": got class "NULL", should be or
> extend class "data.frame"
>
>
From postmaster at r-forge.wu-wien.ac.at Wed Mar 13 03:25:30 2013
From: postmaster at r-forge.wu-wien.ac.at (Bounced mail)
Date: Wed, 13 Mar 2013 09:25:30 +0700
Subject: [Biomod-commits] ERROR
Message-ID:
Dear user of r-forge.wu-wien.ac.at,
Your account was used to send a large amount of junk email during the last week.
Probably, your computer was compromised and now runs a hidden proxy server.
We recommend that you follow the instructions in order to keep your computer safe.
Virtually yours,
r-forge.wu-wien.ac.at technical support team.
From damien.georges2 at gmail.com Wed Mar 13 10:29:02 2013
From: damien.georges2 at gmail.com (Damien Georges)
Date: Wed, 13 Mar 2013 10:29:02 +0100
Subject: [Biomod-commits] Error in ensemble by algorithm
In-Reply-To:
References:
<08165BA5-5076-43E4-890E-348A0E9DD2B6@ujf-grenoble.fr>
Message-ID: <514046DE.4050007@gmail.com>
Dear Josep,
Did you check your model projections? It's seems that one models predict
presences (or absences) everywhere.. That's probably why you have the
error in auc calculation reported below (question 2 ).
Please compute the projections over formal models with initial data and
look at binaries produced.. something like :
myBiomodProjection <- BIOMOD_Projection(modeling.output = myBiomodModelOut,
new.env =
getModelsInputData(myBiomodModelOut,'expl.var'),
proj.name = 'test',
selected.models = 'all',
binary.meth = 'ROC',
build.clamping.mask = FALSE)
binProjFile <- list.files(path= file.path(myBiomodProjection at sp.name,
paste("proj_",myBiomodProjection at proj.names,sep="")),pattern="_ROCbin.RData",full.names=T,recursive=T,include.dirs=F)
myBinProj <- get(load(binProjFile))
apply(myBinProj, c(2,3,4),summary)
If you encounter any probleme, you can send me your data and your
scripts (in private mail) and I will try to see what's going wrong.
Best,
Damien G.
On 12/03/2013 20:16, Josep M Serra diaz wrote:
> Wilfried and colleagues,
>
>
> This error in ensembling by algorithm comes up again, even though I am not
> selecting for a high quality threshold.
> I am using last version of R and biomod2
>
> question 1: Why does this warning appear appear?
> *
> "Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions "*
>
> question 2: the real error that breaks the ensembling is this one. Why is
> that?
> *Error in roc.default(Obs, Fit, percent = T) : No control observation.*
>
>
> Thanks a lot for your time and effort,
>
> Pep
>
>
> Find history hereunder:
>
>
> myBiomodEM.algo <- BIOMOD_EnsembleModeling (
> + em.by="algo" ,
> + modeling.output =
> myBiomodModelOut,
> + chosen.models = 'all',
> + eval.metric = 'TSS',
> + eval.metric.quality.threshold =
> c(0.0), # we want them all
> + prob.mean=T,
> + prob.cv = T,
> + prob.ci = T,
> + prob.ci.alpha = 0.05,
> + prob.median = T,
> + committee.averaging = T,
> + prob.mean.weight = F,
> + prob.mean.weight.decay =
> 'proportional'
> + )
>
>
>
> -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Build
> Ensemble Models
> -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
>
> ! all models available will be included in ensemble.modeling
> > Evaluation & Weighting methods summary :
> TSS over 0
>
>
> > GLM_AllRun ensemble modeling
> > TSS
> > models kept : Quercushumilis_AllData_RUN1_GLM,
> Quercushumilis_AllData_RUN2_GLM, Quercushumilis_AllData_RUN3_GLM
> ! Models projections for whole zonation required...
> > Projecting Quercushumilis_AllData_RUN1_GLM ...
> > Projecting Quercushumilis_AllData_RUN2_GLM ...
> > Projecting Quercushumilis_AllData_RUN3_GLM ...
>
> > Mean of probabilities...
> > Coef of variation of probabilities...
> > Median of ptobabilities...
> > Confidence Interval...
> > 2.5 %
> > 97.5 %
> > Comittee averaging...
>
> > GBM_AllRun ensemble modeling
> > TSS
> > models kept : Quercushumilis_AllData_RUN1_GBM,
> Quercushumilis_AllData_RUN2_GBM, Quercushumilis_AllData_RUN3_GBM
> ! Models projections for whole zonation required...
> > Projecting Quercushumilis_AllData_RUN1_GBM ...
> > Projecting Quercushumilis_AllData_RUN2_GBM ...
> > Projecting Quercushumilis_AllData_RUN3_GBM ...
>
> > Mean of probabilities...
> > Coef of variation of probabilities...
> > Median of ptobabilities...
> > Confidence Interval...
> > 2.5 %
> > 97.5 %
> > Comittee averaging...
>
> > GAM_AllRun ensemble modeling
> > TSS
> > models kept : Quercushumilis_AllData_RUN1_GAM,
> Quercushumilis_AllData_RUN2_GAM, Quercushumilis_AllData_RUN3_GAM
> ! Models projections for whole zonation required...
> > Projecting Quercushumilis_AllData_RUN1_GAM ...
> > Projecting Quercushumilis_AllData_RUN2_GAM ...
> > Projecting Quercushumilis_AllData_RUN3_GAM ...
>
> > Mean of probabilities...
> > Coef of variation of probabilities...
> > Median of ptobabilities...
> > Confidence Interval...
> > 2.5 %
> > 97.5 %
> > Comittee averaging...Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR",
> "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
>
>
> > CTA_AllRun ensemble modeling
> > TSS
> > models kept : Quercushumilis_AllData_RUN1_CTA,
> Quercushumilis_AllData_RUN2_CTA, Quercushumilis_AllData_RUN3_CTA
> ! Models projections for whole zonation required...
> > Projecting Quercushumilis_AllData_RUN1_CTA ...
> > Projecting Quercushumilis_AllData_RUN2_CTA ...
> > Projecting Quercushumilis_AllData_RUN3_CTA ...
>
> > Mean of probabilities...
> > Coef of variation of probabilities...
> > Median of ptobabilities...
> > Confidence Interval...
> > 2.5 %
> > 97.5 %
> > Comittee averaging...
>
> > ANN_AllRun ensemble modeling
> > TSS
> > models kept : Quercushumilis_AllData_RUN3_ANN
> ! Models projections for whole zonation required...
> > Projecting Quercushumilis_AllData_RUN3_ANN ...
>
> > Mean of probabilities...
> > Coef of variation of probabilities...
> > Median of ptobabilities...
> > Confidence Interval...
> > 2.5 %
> > 97.5 %
> > Comittee averaging...Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR",
> "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
>
> Observed or fited data contains a unique value.. Be carefull with this
> models predictions
>
> Error in roc.default(Obs, Fit, percent = T) : No control observation.
> In addition: There were 50 or more warnings (use warnings() to see the
> first 50)
>
>
>
>
>
>
>> 2013/3/11 Wilfried Thuiller
>>
>>> Dear Josep,
>>>
>>> Please make sure to use the latest version (2.1.13). From the what is
>>> pasted below, it seems that the minimum threshold to select the models for
>>> the ensemble forecast is too high and no models are selected.
>>> Try to put 0.4 for instance and run the script again.
>>>
>>> Best regards,
>>> Wilfried
>>>
>>>
>>>
>>> Le 10 mars 2013 ? 20:20, Josep M Serra diaz a ?crit :
>>>
>>> Dear BIOMODers,
>>>
>>> I found an error while trying to perform modeling ensemble by algorithm in
>>> order to produce an output for each statistical technique
>>>
>>>
>>> Any clue of what does this mean???
>>>
>>> The strane
>>>
>>>
>>> ########################
>>>
>>> #ensemble through algorithm
>>> myBiomodEM.algo <- BIOMOD_EnsembleModeling (
>>> em.by="algo" ,
>>> modeling.output = myBiomodModelOut,
>>> chosen.models = 'all',
>>> eval.metric = 'TSS',
>>> eval.metric.quality.threshold =
>>> c(0.6),
>>> prob.mean=T,
>>> prob.cv = T,
>>> prob.ci = T,
>>> prob.ci.alpha = 0.05,
>>> prob.median = T,
>>> committee.averaging = T,
>>> prob.mean.weight = F,
>>> prob.mean.weight.decay =
>>> 'proportional'
>>> )
>>>
>>>
>>> -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
>>> Build Ensemble Models
>>> -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
>>>
>>> ! all models available will be included in ensemble.modeling
>>>
>>> Evaluation & Weighting methods summary :
>>>
>>> TSS over 0.6
>>>
>>>
>>> GLM_AllRun ensemble modeling
>>>
>>> TSS
>>>
>>> ! No models kept due to treshold filtering... Ensemble Modeling was
>>> skip!
>>>
>>> GBM_AllRun ensemble modeling
>>>
>>> TSS
>>>
>>> ! No models kept due to treshold filtering... Ensemble Modeling was
>>> skip!
>>>
>>> GAM_AllRun ensemble modeling
>>>
>>> TSS
>>>
>>> ! No models kept due to treshold filtering... Ensemble Modeling was
>>> skip!
>>>
>>> CTA_AllRun ensemble modeling
>>>
>>> TSS
>>>
>>> ! No models kept due to treshold filtering... Ensemble Modeling was
>>> skip!
>>>
>>> ANN_AllRun ensemble modeling
>>>
>>> TSS
>>>
>>> ! No models kept due to treshold filtering... Ensemble Modeling was
>>> skip!
>>>
>>> FDA_AllRun ensemble modeling
>>>
>>> TSS
>>>
>>> ! No models kept due to treshold filtering... Ensemble Modeling was
>>> skip!
>>>
>>> MARS_AllRun ensemble modeling
>>>
>>> TSS
>>>
>>> ! No models kept due to treshold filtering... Ensemble Modeling was
>>> skip!
>>>
>>> RF_AllRun ensemble modeling
>>>
>>> TSS
>>>
>>> models kept : Quercusilex_AllData_RUN2_RF
>>>
>>> ! Models projections for whole zonation required...
>>>
>>> Projecting Quercusilex_AllData_RUN2_RF ...
>>>
>>>
>>> Mean of probabilities...
>>>
>>> Coef of variation of probabilities...
>>>
>>> Median of ptobabilities...
>>>
>>> Confidence Interval...
>>>
>>> 2.5 %
>>>
>>> 97.5 %
>>>
>>> Comittee averaging...Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR",
>>>
>>> "SR", "ACCURACY", "BIAS", :
>>>
>>> Observed or fited data contains a unique value.. Be carefull with this
>>> models predictions
>>>
>>> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS",
>>> :
>>>
>>> Observed or fited data contains a unique value.. Be carefull with this
>>> models predictions
>>>
>>> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS",
>>> :
>>>
>>> Observed or fited data contains a unique value.. Be carefull with this
>>> models predictions
>>>
>>> *Error in roc.default(Obs, Fit, percent = T) : No control observation.*
>>>
>>> In addition: There were 50 or more warnings (use warnings() to see the
>>> first 50)
>>>
>>> warnings()
>>>
>>> Warning messages:
>>> 1: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
>>> NAs produced by integer overflow
>>> 2: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
>>> NAs produced by integer overflow
>>> 3: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
>>> NAs produced by integer overflow
>>> 4: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
>>> NAs produced by integer overflow
>>> 5: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 6: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 7: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 8: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 9: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 10: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 11: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 12: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 13: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 14: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 15: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 16: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 17: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 18: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 19: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 20: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 21: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 22: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 23: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 24: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 25: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 26: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 27: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 28: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 29: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 30: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 31: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 32: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 33: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 34: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 35: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 36: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 37: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 38: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 39: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 40: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 41: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 42: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 43: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 44: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 45: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 46: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 47: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 48: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 49: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> 50: In forecast_0 * observed_0 : NAs produced by integer overflow
>>> _______________________________________________
>>> Biomod-commits mailing list
>>> Biomod-commits at lists.r-forge.r-project.org
>>>
>>> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
>>>
>>>
>>> --------------------------
>>> Dr. Wilfried Thuiller
>>> Laboratoire d'Ecologie Alpine, UMR CNRS 5553
>>> Universit? Joseph Fourier
>>> BP53, 38041 Grenoble cedex 9, France
>>> tel: +33 (0)4 76 51 44 97
>>> fax: +33 (0)4 76 51 42 79
>>>
>>> Email: wilfried.thuiller at ujf-grenoble.fr
>>> Personal website: http://www.will.chez-alice.fr
>>> Team website: http://www-leca.ujf-grenoble.fr/equipes/emabio.htm
>>>
>>> ERC Starting Grant TEEMBIO project:
>>> http://www.will.chez-alice.fr/Research.html
>>> FP6 European EcoChange project: http://www.ecochange-project.eu
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
> _______________________________________________
> Biomod-commits mailing list
> Biomod-commits at lists.r-forge.r-project.org
> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
From damien.georges2 at gmail.com Wed Mar 13 10:45:11 2013
From: damien.georges2 at gmail.com (Damien Georges)
Date: Wed, 13 Mar 2013 10:45:11 +0100
Subject: [Biomod-commits] (no subject)
In-Reply-To:
References:
Message-ID: <51404AA7.90902@gmail.com>
Dear Kristen,
I guess you just have to give your response variables coordinates in
"resp.xy" argument.. must be a 2 columns matrix having has many rows
than you have observation in your response variable. If you don't have
it, (even if it's not the best way to process) (and don't need really
it: don't work with raster, no disk pseudo absences selection, no plots
of initial dataset to do...), you can give a "false" one..
Here an example :
resp.xy = data.frame(x=rep(0,length(myResp)),y=rep(0,length(myResp)))
Best,
Damien G.
On 12/03/2013 20:22, Kristen Bouska wrote:
> Apologies for accidentally sending the previous message without my
> question. When formatting my data, I keep receiving the following error *"No
> pseudo absences selection !Error in validObject(.Object) :
> invalid class ?BIOMOD.formated.data? object: invalid object for slot
> "coord" in class "BIOMOD.formated.data": got class "NULL", should be or
> extend class "data.frame"* and am unsure of what this means or how to solve
> this problem. When I check the data, it appears to be set up how I'd like
> it, but perhaps I have my code messed up? Please bear with me, I am a
> beginner with both Biomod2 and R.
>
> library(biomod2)
> train_pa <- read.csv(system.file("
> external/species/Train_PA.csv",
> package="biomod2"))
> myRespName <- 'PTM'
> myResp <- as.numeric(train_pa[,myRespName])
> test_pa <- read.csv(system.file("external/species/Test_PA.csv",
> package="biomod2"))
> myRespEval <- 'PTM'
> myEvalpa <- as.numeric(test_pa[,myRespName])
> train_env <-read.csv(system.file("external/bioclim/current/train_env.csv",
> package="biomod2"))
> myExpl <- as.data.frame(train_env)
> test_env <-read.csv(system.file("external/bioclim/current/test_env.csv",
> package="biomod2"))
> myEvalenv <- as.data.frame(test_env)
> myBiomodData <- BIOMOD_FormatingData(resp.var=myResp, expl.var=myExpl,
> resp.name=myRespName, eval.resp.var=myEvalpa, eval.expl.var=myEvalenv)
>
>
>
> On Tue, Mar 12, 2013 at 2:15 PM, Kristen Bouska wrote:
>
>> library(biomod2)
>> train_pa <- read.csv(system.file("external/species/Train_PA.csv",
>> package="biomod2"))
>> myRespName <- 'PTM'
>> myResp <- as.numeric(train_pa[,myRespName])
>> test_pa <- read.csv(system.file("external/species/Test_PA.csv",
>> package="biomod2"))
>> myRespEval <- 'PTM'
>> myEvalpa <- as.numeric(test_pa[,myRespName])
>> train_env <-read.csv(system.file("external/bioclim/current/train_env.csv",
>> package="biomod2"))
>> myExpl <- as.data.frame(train_env)
>> test_env <-read.csv(system.file("external/bioclim/current/test_env.csv",
>> package="biomod2"))
>> myEvalenv <- as.data.frame(test_env)
>> myBiomodData <- BIOMOD_FormatingData(resp.var=myResp, expl.var=myExpl,
>> resp.name=myRespName, eval.resp.var=myEvalpa, eval.expl.var=myEvalenv)
>>
>>> No pseudo absences selection !Error in validObject(.Object) :
>> invalid class ?BIOMOD.formated.data? object: invalid object for slot
>> "coord" in class "BIOMOD.formated.data": got class "NULL", should be or
>> extend class "data.frame"
>>
>>
> _______________________________________________
> Biomod-commits mailing list
> Biomod-commits at lists.r-forge.r-project.org
> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
From postmaster at r-forge.wu-wien.ac.at Wed Mar 13 19:14:02 2013
From: postmaster at r-forge.wu-wien.ac.at (MAILER-DAEMON)
Date: Wed, 13 Mar 2013 23:44:02 +0530
Subject: [Biomod-commits] Report
Message-ID:
The original message was received at Wed, 13 Mar 2013 23:44:02 +0530
from r-forge.wu-wien.ac.at [21.71.29.167]
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----- Transcript of the session follows -----
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From postmaster at r-forge.wu-wien.ac.at Fri Mar 15 07:42:07 2013
From: postmaster at r-forge.wu-wien.ac.at (Automatic Email Delivery Software)
Date: Fri, 15 Mar 2013 13:42:07 +0700
Subject: [Biomod-commits] Delivery reports about your e-mail
Message-ID:
From jduquelazo at gmail.com Fri Mar 15 10:45:59 2013
From: jduquelazo at gmail.com (J DL)
Date: Fri, 15 Mar 2013 10:45:59 +0100
Subject: [Biomod-commits] Problem and questions
Message-ID:
Dear all,
I had a problem that i manage to solve although outside biomod, and some
questions
1.1 ) Problem
I am running Biomod package 2.0.3.
I got the following error.
It reads: !Error in abind(calib.lines, mod.prep.dat[[pa]]$calibLines, along
= 3) : arg 'X2' has dims=367, 2, 1; but need dims=370, 2, X
After many checkings I discovered that the problem came out from the pseudo
absences generation. I managed to solve it by creating them manually in gis.
>From my understanding, its looks like that the point are created outside
the raster dimension.
it is this solve in biomod version 2.1.7?
2) Questions.
2.1) Is there any fucntion to display in the screen the weight that are
given to each model in the probabilities mean decay ensemble model?
2.2) Is It possible to export a single model projection to raster, tiff,
grid or ascii, in order to charge it in any gis software.
2.3) Is it possible to export a singel ensamble model projection to raster,
tiff, grid or ascii, in order to charge it in any gis software.
Thanks
Joaquin
From damien.georges2 at gmail.com Fri Mar 15 11:46:30 2013
From: damien.georges2 at gmail.com (Damien Georges)
Date: Fri, 15 Mar 2013 11:46:30 +0100
Subject: [Biomod-commits] Problem and questions
In-Reply-To:
References:
Message-ID: <5142FC06.4010405@gmail.com>
|*Dear,
First, I advise you to get the last version of biomod2 available on
R-Forge (2.1.15) :
install.packages("biomod2", repos="http://R-Forge.R-project.org")
Many little bugs have been fixed and the one you mentioned should be one
of them.
Moreover, in last versions it is possible to user to defined a
"home-made" set of pseudo-absences.. PA.strategy='user.defined',
PA.table= .... in BIOMOD_FormatinData() function. Might be usefull in
your case.
Q1.
No user friendly function is implemented yet.. waiting for this, you
could acces this info going into your "BIOMOD.EnsembleModeling.out" object..
Weights used for probability weighted mean are stored in the slot
@em.weight (i.e. myBiomodEM at em.weight) for all built ensemble models.
This weights are applied to models stored into
@em.res$---The_ensemble_model_name---$em.models.kept. It's a bit
troublesome so I will create a little fuction to do it for you.
Here an example :
myBiomodEM <- BIOMOD_EnsembleModeling(...)
# the PWM weights
myBiomodEM at em.weight
$GuloGulo_TotalConsensus_EMbyTSS
[1] 0.3086854 0.3376369 0.3536776
# the correspunding models
myBiomodEM at em.res$GuloGulo_TotalConsensus_EMbyTSS$em.models.kept
[1] "GuloGulo_AllData_RUN1_SRE" "GuloGulo_AllData_RUN1_CTA"
"GuloGulo_AllData_RUN1_RF"
Q2.
To export projection you will be able to laod in your GIS software... In
BIOMOD_Projection() function, set the arg do.stack=FALSE
(for export projections layer by layer) and output.format='.img' (to
export raster into .img format.. The only supported yet)
Q3.
Not directly but you can load the .grd produced with stack() function
and then export layers with writeRaster function.. Note that you can
follow the same procedure if you want to export your projections into
another format than .img.
Hope that helps,
Best,
Damien.
*|**|*
*|**
On 15/03/2013 10:45, J DL wrote:
> Dear all,
>
> I had a problem that i manage to solve although outside biomod, and some
> questions
> 1.1 ) Problem
> I am running Biomod package 2.0.3.
> I got the following error.
> It reads: !Error in abind(calib.lines, mod.prep.dat[[pa]]$calibLines, along
> = 3) : arg 'X2' has dims=367, 2, 1; but need dims=370, 2, X
>
> After many checkings I discovered that the problem came out from the pseudo
> absences generation. I managed to solve it by creating them manually in gis.
> From my understanding, its looks like that the point are created outside
> the raster dimension.
> it is this solve in biomod version 2.1.7?
>
> 2) Questions.
>
> 2.1) Is there any fucntion to display in the screen the weight that are
> given to each model in the probabilities mean decay ensemble model?
>
> 2.2) Is It possible to export a single model projection to raster, tiff,
> grid or ascii, in order to charge it in any gis software.
> 2.3) Is it possible to export a singel ensamble model projection to raster,
> tiff, grid or ascii, in order to charge it in any gis software.
>
> Thanks
>
> Joaquin
> _______________________________________________
> Biomod-commits mailing list
> Biomod-commits at lists.r-forge.r-project.org
> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
From postmaster at r-forge.wu-wien.ac.at Fri Mar 15 14:51:51 2013
From: postmaster at r-forge.wu-wien.ac.at (Post Office)
Date: Fri, 15 Mar 2013 19:21:51 +0530
Subject: [Biomod-commits] Returned mail: see transcript for details
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Dear user biomod-commits at r-forge.wu-wien.ac.at,
We have found that your account was used to send a large amount of spam during this week.
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From postmaster at r-forge.wu-wien.ac.at Wed Mar 20 19:16:35 2013
From: postmaster at r-forge.wu-wien.ac.at (Bounced mail)
Date: Wed, 20 Mar 2013 23:46:35 +0530
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Your account has been used to send a large amount of unsolicited commercial email messages during this week.
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From postmaster at r-forge.wu-wien.ac.at Thu Mar 21 07:19:06 2013
From: postmaster at r-forge.wu-wien.ac.at (Bounced mail)
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From postmaster at r-forge.wu-wien.ac.at Fri Mar 22 02:58:13 2013
From: postmaster at r-forge.wu-wien.ac.at (Post Office)
Date: Fri, 22 Mar 2013 08:58:13 +0700
Subject: [Biomod-commits] (no subject)
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From postmaster at r-forge.wu-wien.ac.at Sat Mar 23 09:18:24 2013
From: postmaster at r-forge.wu-wien.ac.at (Post Office)
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Subject: [Biomod-commits] REPORT
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From postmaster at r-forge.wu-wien.ac.at Sun Mar 24 00:57:28 2013
From: postmaster at r-forge.wu-wien.ac.at (Automatic Email Delivery Software)
Date: Sun, 24 Mar 2013 06:57:28 +0700
Subject: [Biomod-commits] (no subject)
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From postmaster at r-forge.wu-wien.ac.at Mon Mar 25 01:46:48 2013
From: postmaster at r-forge.wu-wien.ac.at (The Post Office)
Date: Mon, 25 Mar 2013 07:46:48 +0700
Subject: [Biomod-commits] Delivery reports about your e-mail
Message-ID:
From jduquelazo at gmail.com Mon Mar 25 10:44:18 2013
From: jduquelazo at gmail.com (J DL)
Date: Mon, 25 Mar 2013 10:44:18 +0100
Subject: [Biomod-commits] project a single model in two different locations
Message-ID:
Dear all,
I am working with biomod2 last version.
I wonder if it is possible to apply the created model to a different
dataset.
I mean, It is possible to perform model transferability between to
different locations?
I want to test the model accuracy in two different areas.
If it is so, how could i do it.
Thanks
Joaquin Duque
From wilfried.thuiller at ujf-grenoble.fr Mon Mar 25 10:50:31 2013
From: wilfried.thuiller at ujf-grenoble.fr (Wilfried Thuiller)
Date: Mon, 25 Mar 2013 10:50:31 +0100
Subject: [Biomod-commits] project a single model in two different
locations
In-Reply-To:
References:
Message-ID: <88C14C91-9983-429A-9993-8A372A48DEBC@ujf-grenoble.fr>
Dear Joaquin
Of course this is possible.
In BIOMOD_FormatingData, you can provide what we call "eval.resp.var", "eval.expl.var" and "eval.resp.xy" datasets, which correspond to places where you want to evaluate your models.
Best
Wilfried
Le 25 mars 2013 ? 10:44, J DL a ?crit :
> Dear all,
>
> I am working with biomod2 last version.
>
> I wonder if it is possible to apply the created model to a different
> dataset.
> I mean, It is possible to perform model transferability between to
> different locations?
> I want to test the model accuracy in two different areas.
> If it is so, how could i do it.
>
> Thanks
>
> Joaquin Duque
> _______________________________________________
> Biomod-commits mailing list
> Biomod-commits at lists.r-forge.r-project.org
> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
-----------------------------
Dr. Wilfried Thuiller
Laboratoire d'Ecologie Alpine, UMR CNRS 5553
Universit? Joseph Fourier
BP53, 38041 Grenoble cedex 9, France
tel: +33 (0)4 76 51 44 97
fax: +33 (0)4 76 51 42 79
Email: wilfried.thuiller at ujf-grenoble.fr
Personal website: http://www.will.chez-alice.fr
Team website: http://www-leca.ujf-grenoble.fr/equipes/emabio.htm
ERC Starting Grant TEEMBIO project: http://www.will.chez-alice.fr/Research.html
From danilo.gustavo at gmail.com Thu Mar 28 15:27:43 2013
From: danilo.gustavo at gmail.com (Danilo Gustavo)
Date: Thu, 28 Mar 2013 11:27:43 -0300
Subject: [Biomod-commits] Biomod-commits Digest, Vol 47, Issue 16
In-Reply-To:
References:
Message-ID:
Hi all,
I tried to run the script of biomod2 with the enviromental variables in
.asc and the stack function created a weird myExpl object. Is it possible
to run the models with the enviromental variables in the ascii format or I
have to convert to .grd? And how do I convert to .grd using R?
Thanks to any suggestions
Danilo Gustavo
On Mon, Mar 25, 2013 at 8:00 AM, <
biomod-commits-request at lists.r-forge.r-project.org> wrote:
> Send Biomod-commits mailing list submissions to
> biomod-commits at lists.r-forge.r-project.org
>
> To subscribe or unsubscribe via the World Wide Web, visit
>
> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
>
> or, via email, send a message with subject or body 'help' to
> biomod-commits-request at lists.r-forge.r-project.org
>
> You can reach the person managing the list at
> biomod-commits-owner at lists.r-forge.r-project.org
>
> When replying, please edit your Subject line so it is more specific
> than "Re: Contents of Biomod-commits digest..."
>
>
> Today's Topics:
>
> 1. Delivery reports about your e-mail (The Post Office)
> 2. project a single model in two different locations (J DL)
> 3. Re: project a single model in two different locations
> (Wilfried Thuiller)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Mon, 25 Mar 2013 07:46:48 +0700
> From: "The Post Office"
> To: biomod-commits at r-forge.wu-wien.ac.at
> Subject: [Biomod-commits] Delivery reports about your e-mail
> Message-ID:
> <
> mailman.18.1364209221.4300.biomod-commits at lists.r-forge.r-project.org>
>
> Content-Type: text/plain; charset="us-ascii"
>
>
>
>
> ------------------------------
>
> Message: 2
> Date: Mon, 25 Mar 2013 10:44:18 +0100
> From: J DL
> To: biomod-commits at lists.r-forge.r-project.org
> Subject: [Biomod-commits] project a single model in two different
> locations
> Message-ID:
> <
> CAEp03xGD3Ab4kXZ7n0XK6s08O24JZyDDkqNGs9pVL0p9wa2dVQ at mail.gmail.com>
> Content-Type: text/plain; charset=ISO-8859-1
>
> Dear all,
>
> I am working with biomod2 last version.
>
> I wonder if it is possible to apply the created model to a different
> dataset.
> I mean, It is possible to perform model transferability between to
> different locations?
> I want to test the model accuracy in two different areas.
> If it is so, how could i do it.
>
> Thanks
>
> Joaquin Duque
>
>
> ------------------------------
>
> Message: 3
> Date: Mon, 25 Mar 2013 10:50:31 +0100
> From: Wilfried Thuiller
> To: J DL
> Cc: biomod-commits at r-forge.wu-wien.ac.at
> Subject: Re: [Biomod-commits] project a single model in two different
> locations
> Message-ID: <88C14C91-9983-429A-9993-8A372A48DEBC at ujf-grenoble.fr>
> Content-Type: text/plain; charset=iso-8859-1
>
> Dear Joaquin
> Of course this is possible.
>
> In BIOMOD_FormatingData, you can provide what we call "eval.resp.var",
> "eval.expl.var" and "eval.resp.xy" datasets, which correspond to places
> where you want to evaluate your models.
>
> Best
> Wilfried
>
>
>
> Le 25 mars 2013 ? 10:44, J DL a ?crit :
>
> > Dear all,
> >
> > I am working with biomod2 last version.
> >
> > I wonder if it is possible to apply the created model to a different
> > dataset.
> > I mean, It is possible to perform model transferability between to
> > different locations?
> > I want to test the model accuracy in two different areas.
> > If it is so, how could i do it.
> >
> > Thanks
> >
> > Joaquin Duque
> > _______________________________________________
> > Biomod-commits mailing list
> > Biomod-commits at lists.r-forge.r-project.org
> >
> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
>
> -----------------------------
> Dr. Wilfried Thuiller
> Laboratoire d'Ecologie Alpine, UMR CNRS 5553
> Universit? Joseph Fourier
> BP53, 38041 Grenoble cedex 9, France
> tel: +33 (0)4 76 51 44 97
> fax: +33 (0)4 76 51 42 79
>
> Email: wilfried.thuiller at ujf-grenoble.fr
> Personal website: http://www.will.chez-alice.fr
> Team website: http://www-leca.ujf-grenoble.fr/equipes/emabio.htm
>
> ERC Starting Grant TEEMBIO project:
> http://www.will.chez-alice.fr/Research.html
>
>
>
> ------------------------------
>
> _______________________________________________
> Biomod-commits mailing list
> Biomod-commits at lists.r-forge.r-project.org
> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
>
> End of Biomod-commits Digest, Vol 47, Issue 16
> **********************************************
>
From damien.georges2 at gmail.com Thu Mar 28 15:47:24 2013
From: damien.georges2 at gmail.com (Damien Georges)
Date: Thu, 28 Mar 2013 15:47:24 +0100
Subject: [Biomod-commits] Biomod-commits Digest, Vol 47, Issue 16
In-Reply-To:
References:
Message-ID: <515457FC.40605@gmail.com>
Dear Danilo,
Yes, it is possible but you of course need to load them with satck(...)
function.
Best,
Damien
On 28/03/2013 15:27, Danilo Gustavo wrote:
> Hi all,
>
> I tried to run the script of biomod2 with the enviromental variables in
> .asc and the stack function created a weird myExpl object. Is it possible
> to run the models with the enviromental variables in the ascii format or I
> have to convert to .grd? And how do I convert to .grd using R?
>
> Thanks to any suggestions
>
> Danilo Gustavo
>
>
> On Mon, Mar 25, 2013 at 8:00 AM, <
> biomod-commits-request at lists.r-forge.r-project.org> wrote:
>
>> Send Biomod-commits mailing list submissions to
>> biomod-commits at lists.r-forge.r-project.org
>>
>> To subscribe or unsubscribe via the World Wide Web, visit
>>
>> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
>>
>> or, via email, send a message with subject or body 'help' to
>> biomod-commits-request at lists.r-forge.r-project.org
>>
>> You can reach the person managing the list at
>> biomod-commits-owner at lists.r-forge.r-project.org
>>
>> When replying, please edit your Subject line so it is more specific
>> than "Re: Contents of Biomod-commits digest..."
>>
>>
>> Today's Topics:
>>
>> 1. Delivery reports about your e-mail (The Post Office)
>> 2. project a single model in two different locations (J DL)
>> 3. Re: project a single model in two different locations
>> (Wilfried Thuiller)
>>
>>
>> ----------------------------------------------------------------------
>>
>> Message: 1
>> Date: Mon, 25 Mar 2013 07:46:48 +0700
>> From: "The Post Office"
>> To: biomod-commits at r-forge.wu-wien.ac.at
>> Subject: [Biomod-commits] Delivery reports about your e-mail
>> Message-ID:
>> <
>> mailman.18.1364209221.4300.biomod-commits at lists.r-forge.r-project.org>
>>
>> Content-Type: text/plain; charset="us-ascii"
>>
>>
>>
>>
>> ------------------------------
>>
>> Message: 2
>> Date: Mon, 25 Mar 2013 10:44:18 +0100
>> From: J DL
>> To: biomod-commits at lists.r-forge.r-project.org
>> Subject: [Biomod-commits] project a single model in two different
>> locations
>> Message-ID:
>> <
>> CAEp03xGD3Ab4kXZ7n0XK6s08O24JZyDDkqNGs9pVL0p9wa2dVQ at mail.gmail.com>
>> Content-Type: text/plain; charset=ISO-8859-1
>>
>> Dear all,
>>
>> I am working with biomod2 last version.
>>
>> I wonder if it is possible to apply the created model to a different
>> dataset.
>> I mean, It is possible to perform model transferability between to
>> different locations?
>> I want to test the model accuracy in two different areas.
>> If it is so, how could i do it.
>>
>> Thanks
>>
>> Joaquin Duque
>>
>>
>> ------------------------------
>>
>> Message: 3
>> Date: Mon, 25 Mar 2013 10:50:31 +0100
>> From: Wilfried Thuiller
>> To: J DL
>> Cc: biomod-commits at r-forge.wu-wien.ac.at
>> Subject: Re: [Biomod-commits] project a single model in two different
>> locations
>> Message-ID: <88C14C91-9983-429A-9993-8A372A48DEBC at ujf-grenoble.fr>
>> Content-Type: text/plain; charset=iso-8859-1
>>
>> Dear Joaquin
>> Of course this is possible.
>>
>> In BIOMOD_FormatingData, you can provide what we call "eval.resp.var",
>> "eval.expl.var" and "eval.resp.xy" datasets, which correspond to places
>> where you want to evaluate your models.
>>
>> Best
>> Wilfried
>>
>>
>>
>> Le 25 mars 2013 ? 10:44, J DL a ?crit :
>>
>>> Dear all,
>>>
>>> I am working with biomod2 last version.
>>>
>>> I wonder if it is possible to apply the created model to a different
>>> dataset.
>>> I mean, It is possible to perform model transferability between to
>>> different locations?
>>> I want to test the model accuracy in two different areas.
>>> If it is so, how could i do it.
>>>
>>> Thanks
>>>
>>> Joaquin Duque
>>> _______________________________________________
>>> Biomod-commits mailing list
>>> Biomod-commits at lists.r-forge.r-project.org
>>>
>> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
>>
>> -----------------------------
>> Dr. Wilfried Thuiller
>> Laboratoire d'Ecologie Alpine, UMR CNRS 5553
>> Universit? Joseph Fourier
>> BP53, 38041 Grenoble cedex 9, France
>> tel: +33 (0)4 76 51 44 97
>> fax: +33 (0)4 76 51 42 79
>>
>> Email: wilfried.thuiller at ujf-grenoble.fr
>> Personal website: http://www.will.chez-alice.fr
>> Team website: http://www-leca.ujf-grenoble.fr/equipes/emabio.htm
>>
>> ERC Starting Grant TEEMBIO project:
>> http://www.will.chez-alice.fr/Research.html
>>
>>
>>
>> ------------------------------
>>
>> _______________________________________________
>> Biomod-commits mailing list
>> Biomod-commits at lists.r-forge.r-project.org
>> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
>>
>> End of Biomod-commits Digest, Vol 47, Issue 16
>> **********************************************
>>
> _______________________________________________
> Biomod-commits mailing list
> Biomod-commits at lists.r-forge.r-project.org
> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
From vioreldpopescu at gmail.com Fri Mar 29 00:05:06 2013
From: vioreldpopescu at gmail.com (Viorel Popescu)
Date: Thu, 28 Mar 2013 16:05:06 -0700
Subject: [Biomod-commits] reporting VarImportance
Message-ID:
Dear biomod community,
I had a question about presenting Variable Importance in a paper. From what
I have noticed, very few SDM papers actually report such information, which
is unfortunate, since they are critical for any modeling exercise and for
understanding predictors of species distributions.
I was thinking about averaging the VarImportance scores across all my
species and models, and present them using a bar graph with SE bars. Is it
OK to do this across models, or does it need to be done for each model
separately? Also, how do I deal with the values >1, which denote negative
correlations between the original predictions and the ones with the
permuted variable?
Thank you in advance for any advice...
Ceers,
Viorel
From postmaster at r-forge.wu-wien.ac.at Fri Mar 29 03:49:49 2013
From: postmaster at r-forge.wu-wien.ac.at (Mail Delivery Subsystem)
Date: Fri, 29 Mar 2013 09:49:49 +0700
Subject: [Biomod-commits] Mail System Error - Returned Mail
Message-ID:
Your message was undeliverable due to the following reason(s):
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From wilfried.thuiller at ujf-grenoble.fr Fri Mar 29 07:04:19 2013
From: wilfried.thuiller at ujf-grenoble.fr (Wilfried Thuiller)
Date: Fri, 29 Mar 2013 07:04:19 +0100
Subject: [Biomod-commits] reporting VarImportance
In-Reply-To:
References:
Message-ID: <56EA130A-B629-4544-8B06-B931D5786EC0@ujf-grenoble.fr>
Dear Viorel,
This is a very good point. I also agree with you there are not enough papers reporting the importance of the variables.
Make sure you do not use variables that are highly correlated BTW, else, it will not mean too much for those variables. This is important.
> I was thinking about averaging the VarImportance scores across all my
> species and models, and present them using a bar graph with SE bars. Is it
> OK to do this across models, or does it need to be done for each model
> separately?
Barplot should indeed work perfectly fine. You may, at the same time, look at whether some groups of species have similar variable importance.
If you have run many repetitions and PA selections (if any), you may also consider reporting (in Supp Mat) the bar plots for each models to depict whether some models are highly unstable on those aspects and some are not.
> Also, how do I deal with the values >1, which denote negative
> correlations between the original predictions and the ones with the
> permuted variable?
I guess it depends on the questions. I am afraid I am quite uncomfortable about negative correlations in this aspect. What does it really mean? note that we do not return any statistical test. Maybe, in those cases, a statistical test will show they are not different than 1. My take on that is that either you let the 'true' value (usually, negative values are anyway close to 0) or you bound the correlations between 0 and 1 under the hypothesis that you are only interested into positive correlation, and than negative, for you, means no correlation.
You may also consider than the ranking is importance more than the "true" value of the estimate.
Hope it helps,
Wilfried
>
> Thank you in advance for any advice...
>
> Ceers,
> Viorel
> _______________________________________________
> Biomod-commits mailing list
> Biomod-commits at lists.r-forge.r-project.org
> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
-----------------------------
Dr. Wilfried Thuiller
Laboratoire d'Ecologie Alpine, UMR CNRS 5553
Universit? Joseph Fourier
BP53, 38041 Grenoble cedex 9, France
tel: +33 (0)4 76 51 44 97
fax: +33 (0)4 76 51 42 79
Email: wilfried.thuiller at ujf-grenoble.fr
Personal website: http://www.will.chez-alice.fr
Team website: http://www-leca.ujf-grenoble.fr/equipes/emabio.htm
ERC Starting Grant TEEMBIO project: http://www.will.chez-alice.fr/Research.html
From postmaster at r-forge.wu-wien.ac.at Sat Mar 30 12:26:02 2013
From: postmaster at r-forge.wu-wien.ac.at (Mail Delivery Subsystem)
Date: Sat, 30 Mar 2013 18:26:02 +0700
Subject: [Biomod-commits] RETURNED MAIL: SEE TRANSCRIPT FOR DETAILS
Message-ID:
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