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Updated: 1 hour 42 min ago

Re: GEOSTAT Summer School for PhD students | 13-19 August 2017 | MEDILS Split

Tue, 04/11/2017 - 08:08
MEMO: This is to remind you that the registrations for GEOSTAT Summer
School 13-19 August 2017 | MEDILS Split, will close on:

- 15th of April 2017

To register for GEOSTAT visit:

https://geostat-course.org/2017

PS: Current number of registrations: 44 (this summer school is limited
to 55 participants).

T. Hengl
http://www.wageningenur.nl/en/Persons/dr.-T-Tom-Hengl.htm


On 06/03/17 19:58, Tomislav Hengl wrote:
>
> The GEOSTAT Split 2017 Summer School will be held this year at the
> Mediterranean Institute for Life Sciences (MEDILS) Split, Croatia in
> the period 13-19 August 2017. MEDILS is an international centre of
> excellence for molecular biology and is located in a nature park in a
> near proximity of the historic town Split. It has in-house
> accommodation and numerous facilities including its own park and sport
> facilities.
>
> The registrations are now open. To register for this Summer School
> please visit:
>
> https://geostat-course.org/2017
>
> This summer school is limited to 55 participants. In the case of
> higher number of registrations, applicants will be ranked based on (1)
> their contribution to the FOSS, (2) academic output, (3) distance to
> the venue and (4) time of registration. The registrations fees are
> usually around 550 EUR (invoice will be sent after registrations).
> Registration fees cover costs of using facilities, lunch and coffee
> breaks and costs of travel and accommodation for lecturers.
> Participants from ODA countries (employed by an organization or
> company in ODA-listed country) typically receive a subsidized price
> for the accommodation and registration costs. GEOSTAT makes no profit.
> All lecturers are volunteers. None of the lecturers receives any
> honorarium payment or is contracted by the local organizers.
>
> This years topics include:
> - Application of R+OSGeo tools in: spatio-temporal monitoring,
> geostatistical mapping, point pattern analysis, epidemiology...
> - Machine Learning methods for spatial and spatio-temporal data,
> - QGIS and RQGIS tutorials,
> - RMarkdown/Sweave tutorials,
> - Visualization of spatio-temporal data,
>
> Lecturers:
> - Barry Rowlingson (Lancaster Medical School, Lancaster University),
> - Virgilio Gomez Rubio (Deparment of Mathematics, Universidad de
> Castilla-La Mancha),
> - Jannes Muenchow (Department of GIScience, University of Jena),
> - Daniel Nüst (Institute for Geoinformatics, University of Münster)
> - Tomislav Hengl (ISRIC - World Soil Information),
>
> Registration deadline is:
>
> - April 15th 2017
>
> hope to see you in Split,
>
> T. Hengl
> http://www.wageningenur.nl/en/Persons/dr.-T-Tom-Hengl.htm
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variance-covariance matrix for GMerrorsar

Mon, 04/10/2017 - 18:27
Dear list,

I want to use bootstrapping to derive confidence intervals for marginal
wtp after GMerrorsar command.

It works for stsls since covariance matrix is directly available. However,
I cannot find covariance matrix for GMerrorsar.

For example, the following code works for stsls:

model1.beta <- coef(model1)

model1.vcov <- summary(model1)$var

model1.sim <- rmultnorm(10000, mu = model1.beta, vmat = model1.vcov)

model1.mwtp <- model1.sim * Pbar

model1.ci <- apply(model1.mwtp, 2, quantile, c(0.025, 0.975))


when I apply the same code for GMerrorsar:


model2.beta <- coef(model2)

model2.vcov <- summary(model2)$var


> model2.vcov

NULL


How can I obtain covariance matrix for GMerrorsar?


Chelsea

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Re: Ordinary co-kriging with gstat - computing time/syntax error?

Mon, 04/10/2017 - 12:03
You need to set nmax for each variable, as a parameter of the gstat()
function call. I don't see nmax mentioned anywhere in the documentation
of gstat::predict.

On 10/04/17 18:16, Mercedes Román wrote:
> Dear R-sig-Geo members,
>
> I want to perform co-kriging on the residuals on two regression models with
> gstat. I am following some tutorials (Rossiter, 2012; Malone et al., 2017),
> but I may have a mistake in the script. The computing time seems too long
> (I know it takes a lot of calculation time, but in four days it never
> finished), even when I set nmax=20. Originally, I wanted to perform a test
> on ~ 11000 points, but I am trying now with a subset of 200 points. My
> training dataset has ~ 27000 observations. I would like to produce a
> reproducible example, but perhaps just the sintaxis of the script may be
> enough to have a clue of my problem. My computer has an Intel®Xeon® CPU
> E5-2609 v2 processor, and 32Go RAM memory.
>
> Let’s say my two response variables are X1 and X2, and the dataframes with
> the coordinates and covariates are “training” and “test”.
>
> ### Calculate the predictions on the training dataset (predictors is a
> dataframe with the covariates)
> training$pred.X1 <- predict(modelX1, newdata = predictors)
> training$pred.X2 <- predict(modelX2, newdata = predictors)
> ### Calculate residuals
> training$res.X1<- training$X1 - training$ pred.X1
> training$res.X2 <- training$X2 - training$pred.X2
> ### Transform to spatial
> training.sp <- training
> coordinates(training.sp) <- ~ x + y
> proj4string(training.sp) <- CRS("+init=epsg:2154")
>
> ### Define gstat object and compute experimental semivariograms for X1 and
> X2 (for visual examination)
> X1.g <- gstat(formula = res.X1~1, data = training.sp)
> X1.svar <- variogram(X1.g, width = 5000, cutoff=250000)
> X1.ivgm <- vgm(nugget=0.56, range=14229, psill=0.65, kappa=10, model="Mat")
> ## initial variogram model
> X1.vgm <- fit.variogram(X1.svar, model=X1.ivgm, fit.method=7)
> plot(X1.svar, X1.vgm)
>
> X2.g <- gstat(formula = res.X2~1, data = training.sp)
> X2.svar <- variogram(X2.g, width = 5000, cutoff=250000)
> X2.ivgm <- vgm(nugget=0.19, range=13954, psill=0.21,kappa=10,   model="Mat")
> X2.vgm <- fit.variogram(X2.svar, model=X2.ivgm, fit.method=7)
> plot(X2.svar, X2.vgm)
>
> ### Define gstat object to compute the direct variograms and covariogram
> g <- gstat( NULL, id="res.X1",  formula = res.X1~1, data = training.sp)
> g <- gstat( g, id="res.X2",  formula = res.X2~1, data = training.sp)
> v.cross <- variogram(g, width=5000, cutoff = 250000)
> #### Fill parameters
> g <- gstat(g, id = "res.X2", model = X2.vgm,  fill.all=T)
> ### fit LMCR
> g <- fit.lmc(v.cross, g)
> ### Predict at the test locations (test.sp only indicates the coordinates)
> test.sp <- test
> coordinates(ensemble.sp) <- ~ x + y
> proj4string(ensemble.sp) <- CRS("+init=epsg:2154")
> k.c_residuals <- predict(g, test.sp, nmax=20)
>
> I am wondering if there is something missing in the predict command… I do
> not need to include the training data in it?
> I have been reading previous posts from R-sig-Geo, but I still did not find
> the solution. I would appreciate any advice you could give me.
>
> Thanks,
>
> Mercedes Roman
>
> INRA
>
> Unité de Service InfoSol
>
> 2163, avenue de la Pomme de Pin
>
> CS 40001 Ardon
>
> 45075 ORLEANS cedex 2
>
> France
>
> [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-Geo mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
> --
Edzer Pebesma
Institute for Geoinformatics  (ifgi),  University of Münster
Heisenbergstraße 2, 48149 Münster, Germany; +49 251 83 33081
Journal of Statistical Software:   http://www.jstatsoft.org/
Computers & Geosciences:   http://elsevier.com/locate/cageo/


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Ordinary co-kriging with gstat - computing time/syntax error?

Mon, 04/10/2017 - 11:16
Dear R-sig-Geo members,

I want to perform co-kriging on the residuals on two regression models with
gstat. I am following some tutorials (Rossiter, 2012; Malone et al., 2017),
but I may have a mistake in the script. The computing time seems too long
(I know it takes a lot of calculation time, but in four days it never
finished), even when I set nmax=20. Originally, I wanted to perform a test
on ~ 11000 points, but I am trying now with a subset of 200 points. My
training dataset has ~ 27000 observations. I would like to produce a
reproducible example, but perhaps just the sintaxis of the script may be
enough to have a clue of my problem. My computer has an Intel®Xeon® CPU
E5-2609 v2 processor, and 32Go RAM memory.

Let’s say my two response variables are X1 and X2, and the dataframes with
the coordinates and covariates are “training” and “test”.

### Calculate the predictions on the training dataset (predictors is a
dataframe with the covariates)
training$pred.X1 <- predict(modelX1, newdata = predictors)
training$pred.X2 <- predict(modelX2, newdata = predictors)
### Calculate residuals
training$res.X1<- training$X1 - training$ pred.X1
training$res.X2 <- training$X2 - training$pred.X2
### Transform to spatial
training.sp <- training
coordinates(training.sp) <- ~ x + y
proj4string(training.sp) <- CRS("+init=epsg:2154")

### Define gstat object and compute experimental semivariograms for X1 and
X2 (for visual examination)
X1.g <- gstat(formula = res.X1~1, data = training.sp)
X1.svar <- variogram(X1.g, width = 5000, cutoff=250000)
X1.ivgm <- vgm(nugget=0.56, range=14229, psill=0.65, kappa=10, model="Mat")
## initial variogram model
X1.vgm <- fit.variogram(X1.svar, model=X1.ivgm, fit.method=7)
plot(X1.svar, X1.vgm)

X2.g <- gstat(formula = res.X2~1, data = training.sp)
X2.svar <- variogram(X2.g, width = 5000, cutoff=250000)
X2.ivgm <- vgm(nugget=0.19, range=13954, psill=0.21,kappa=10,   model="Mat")
X2.vgm <- fit.variogram(X2.svar, model=X2.ivgm, fit.method=7)
plot(X2.svar, X2.vgm)

### Define gstat object to compute the direct variograms and covariogram
g <- gstat( NULL, id="res.X1",  formula = res.X1~1, data = training.sp)
g <- gstat( g, id="res.X2",  formula = res.X2~1, data = training.sp)
v.cross <- variogram(g, width=5000, cutoff = 250000)
#### Fill parameters
g <- gstat(g, id = "res.X2", model = X2.vgm,  fill.all=T)
### fit LMCR
g <- fit.lmc(v.cross, g)
### Predict at the test locations (test.sp only indicates the coordinates)
test.sp <- test
coordinates(ensemble.sp) <- ~ x + y
proj4string(ensemble.sp) <- CRS("+init=epsg:2154")
k.c_residuals <- predict(g, test.sp, nmax=20)

I am wondering if there is something missing in the predict command… I do
not need to include the training data in it?
I have been reading previous posts from R-sig-Geo, but I still did not find
the solution. I would appreciate any advice you could give me.

Thanks,

Mercedes Roman

INRA

Unité de Service InfoSol

2163, avenue de la Pomme de Pin

CS 40001 Ardon

45075 ORLEANS cedex 2

France

        [[alternative HTML version deleted]]

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Question niche based model whith R

Mon, 04/10/2017 - 07:16

You might check out the dismo package and the vignette it has on species distribution modeling.

https://cran.r-project.org/web/packages/dismo/index.html

Tim


   
Date: Mon, 10 Apr 2017 09:12:54 +0000 (UTC)
From: Soufianou Abou <[hidden email]>
To: "[hidden email]" <[hidden email]>
Subject: [R-sig-Geo] Question  niche based model whith R
Message-ID: <[hidden email]>
Content-Type: text/plain; charset="UTF-8"

?Hello dears,I work on the modeling of the ecological niche of striga, I have presence data (data) and bioclimatic variables and other variables around.Indeed, my question how should I proceed to model the ecological niche with software R??Pending receipt of  my request, please accept my best regards.SADDA Abou-Soufianou -------------------------------------- DoctorantUniversit? Dan Dicko Dankoulodo de Maradi,120 avenue Maman Koraou,ADS, MaradiBP 465 Maradi/Niger E-mail:?[hidden email]?: (+227)96269987
        [[alternative HTML version deleted]]

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SDM course in the beautiful city of Evora, Portugal

Mon, 04/10/2017 - 06:37
Dear List,

This is to inform you that the 4th edition of the PhD/Postdoc course
“Species distributions models: concepts, methods, applications, and
challenges” is organised by Prof. Miguel Araùjo and Dr. Babak Naimi in
University of Evora, Portugal between 22th and 28th of May, 2017.

The course aims to introduce the fundamental concepts underpinning
ecological niche models (ENM), describing some of the most prominent
methods currently in use, and discussing the strengths and limitations of
these models for different applications. The course gives equal weight to
theory and application. The students will have the opportunity to learn how
to run ENM with the new R package, sdm, that is an object-oriented,
reproducible and extensible R platform for species distribution modelling (
http://www.biogeoinformatics.org/) that has been developed by the
organisers of the course.

For more details about the course and the registration, check the following
website:

http://www.maraujolab.com/2017-species-distributions-modelling-course/

You can also download the paper introduces the sdm R package from the
following link:

http://onlinelibrary.wiley.com/doi/10.1111/ecog.01881/full

Please feel free to forward this message to anyone who may be interested.

Best regards,

Babak Naimi

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Re: Question niche based model whith R

Mon, 04/10/2017 - 06:34
There are several packages available in R to do ecological niche modelling.
One easy-to-use option would be the sdm package. It is a comprehensive
modelling and simulation framework to fit species distribution models
supports several algorithms, and also is user-friendly. You can find a
tutorial for a quick start at: http://biogeoinformatics.org

Good luck,
Babak


>
> Date: Mon, 10 Apr 2017 09:12:54 +0000 (UTC)
> From: Soufianou Abou <[hidden email]>
> To: "[hidden email]" <[hidden email]>
> Subject: [R-sig-Geo] Question  niche based model whith R
> Message-ID: <[hidden email]>
> Content-Type: text/plain; charset="UTF-8"
>
> ?Hello dears,I work on the modeling of the ecological niche of striga, I
> have presence data (data) and bioclimatic variables and other variables
> around.Indeed, my question how should I proceed to model the ecological
> niche with software R??Pending receipt of my request, please accept my best
> regards.SADDA Abou-Soufianou --------------------------------------
> DoctorantUniversit? Dan Dicko Dankoulodo de Maradi,120 avenue Maman
> Koraou,ADS, MaradiBP 465 Maradi/Niger E-mail:?[hidden email]?:
> (+227)96269987 <+227%2096%2026%2099%2087>
>         [[alternative HTML version deleted]]
>
>
>
>
>
        [[alternative HTML version deleted]]

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Re: Question niche based model with R

Mon, 04/10/2017 - 05:29
Hi Abou,
it is a quite broad question...a simple and easy answer would be "you
can just model yous data.frame with ?lm function or ?glm but you would
lose many of the iteresting stuff!! Then, in my opinion, first of all
you must decide between "presence-only" and "presence-absence" models
(my suggestion is for the second and in your case you must learn how
to generate pseudo-absences or background points. So, in my
experience, there are two main packages which are the most interesting
and suited for this purpose and which may help you: dismo and biomod2
(https://cran.r-project.org/web/packages/dismo/dismo.pdf -
https://cran.r-project.org/web/packages/biomod2/biomod2.pdf). There
are also many tutorials which can help you, for instance
https://cran.r-project.org/web/packages/dismo/vignettes/sdm.pdf
Then once you will handle the subject you will also be able to create
your own SDM using the algorithm you prefer (e.g. RandomForest,
MaxEnt, GLM, MARS, GAM and so on...) and without any additional
package. Anyway, my advice is to use one of the two above cited
packages because they are object-oriented and optimized for Ecological
Modelling.

Just to cover possible next question (which model is the best
solution) you will see that it is impossible to define the BEST method
which can perform correctly in all the circumstances. Many Researchers
are used to work with a single-algorithm (for example Generalized
Linear Models, Maximum Entropy, Generalized Additive Models, Neural
network, Random Forest etc.) while others are more willing to work
with ensemble projections and consensus models (i.e. mean or weighted
mean of more than one algorithm). Some examples of single-model and
ensemble model are here:

http://dx.doi.org/10.5424/fs/2016253-09476
http://dx.doi.org/10.1111/gcb.12604
http://dx.doi.org/10.1007/s13595-014-0439-4
http://dx.doi.org/10.1111/gcb.12476

Cheers,

--
Maurizio Marchi
Skype ID: maurizioxyz
linux user 552742

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Question niche based model whith R

Mon, 04/10/2017 - 04:12
 Hello dears,I work on the modeling of the ecological niche of striga, I have presence data (data) and bioclimatic variables and other variables around.Indeed, my question how should I proceed to model the ecological niche with software R? Pending receipt of my request, please accept my best regards.SADDA Abou-Soufianou -------------------------------------- DoctorantUniversité Dan Dicko Dankoulodo de Maradi,120 avenue Maman Koraou,ADS, MaradiBP 465 Maradi/Niger E-mail: [hidden email] : (+227)96269987
        [[alternative HTML version deleted]]

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