Dear R users,
A new version (1.2.0) of the “spm” package for spatial predictive modelling is
now available on CRAN.
The introductory vignette is available here:
https://cran.rstudio.com/web/packages/spm/vignettes/spm.html
In this version, two additional functions, avi and rvi have been
Hi Waldir,
Please check library(spm). The function RFcv and rgcv in library(spm) provide
you better options to assess the performance of random forest than using OOB
error.
Kind regards,
Jin
-Original Message-
From: R-sig-Geo [mailto:r-sig-geo-boun...@r-project.org] On Behalf Of
Hi All,
I tried to use xgboost to model and predict count data. The predictions are
however not as expected as shown below.
# sponge count data in library(spm)
library(spm)
data(sponge)
data(sponge.grid)
names(sponge)
[1] "easting" "northing" "sponge" "tpi3" "var7" "entro7"
Dear R users,
A new version (1.1.0) of the “spm” package for spatial predictive modelling is
now available on CRAN.
The introductory vignette is available here:
https://cran.rstudio.com/web/packages/spm/vignettes/spm.html
There are several new enhancements to the package including a
Agreed, Michael. Please the refs provided for some demonstrations at a
latitudinal gradient.
From: Michael Sumner [mailto:mdsum...@gmail.com]
Sent: Thursday, 22 February 2018 11:26 PM
To: Li Jin
Cc: Dominik Schneider; r-sig-geo@r-project.org
Subject: [DKIM] Re: [R-sig-Geo] [DKIM] Re
projection
systems. The references provided demonstrated that the commonly used WGS84 is
as good as relevant projection systems.
From: Dominik Schneider [mailto:dominik.schnei...@colorado.edu]
Sent: Wednesday, 21 February 2018 5:02 AM
To: Li Jin
Cc: Stefano Sofia; r-sig-geo@r-project.org
Subject
The effects of spatial reference systems on interpolations and accuracy are
minimal, and lat and long can be used. Please see the following studies for
details.
Jiang, W., Li, J., 2013. Are Spatial Modelling Methods Sensitive to Spatial
Reference Systems for Predicting Marine Environmental
...@hotmail.com]
Sent: Thursday, 23 November 2017 10:01 AM
To: Li Jin; Tomislav Hengl; r-sig-geo@r-project.org
Subject: [DKIM] Re: [DKIM] Re: [R-sig-Geo] Fw: Why is there a large predictive
difference for Univ. Kriging? [SEC=UNCLASSIFIED]
Jin,
Is there any to get the variances of the predictions in spm
Let try spm and see what we can achieve. All these scripts were directly
modified from examples in spm.
> library(spm)
> library(sp)
> library(gstat)
> data(meuse)
> set.seed(999)
> rfcv1 <- RFcv(meuse[, c(5,4,7,8)], meuse[, 6], predacc = "ALL") # I used the
> same predictors in the same order
: Wednesday, 22 November 2017 5:38 PM
To: Li Jin; r-sig-geo@r-project.org
Subject: Re: [DKIM] Re: [R-sig-Geo] [DKIM] Fw: Why is there a large predictive
difference forUniv. Kriging? [SEC=UNCLASSIFIED]
Jin,
do you think there is potential evidence of overfitting for KED given the large
For both models, the MAE for holdout is larger than that for the training. That
is expected.
From: Joelle k. Akram [mailto:chino_to...@hotmail.com]
Sent: Wednesday, 22 November 2017 12:49 PM
To: Li Jin; r-sig-geo@r-project.org
Subject: Re: [DKIM] Re: [R-sig-Geo] [DKIM] Fw: Why is there a large
BTW, to your question, the first MAE is measuring the goodness of fit, the
second measuring the predictive accuracy. The second paper below has partially
address this.
-Original Message-
From: R-sig-Geo [mailto:r-sig-geo-boun...@r-project.org] On Behalf Of Li Jin
Sent: Wednesday, 22
They are not yet.
From: Joelle k. Akram [mailto:chino_to...@hotmail.com]
Sent: Wednesday, 22 November 2017 11:56 AM
To: Li Jin; r-sig-geo@r-project.org
Subject: [DKIM] Re: [DKIM] [R-sig-Geo] Fw: Why is there a large predictive
difference forUniv. Kriging? [SEC=UNCLASSIFIED]
Hi Jin,
thank
Hi Chris,
The UK used here is usually called kriging with an external drift (KED). It, in
fact, is a linear model plus kriging, which assumes linear relationship that is
usually not true. It has been tested in several studies and was outperformed by
machine learning methods like RF, RFOK, RFIDW
Hi All,
Just thought you might be interested in a recently released R package, spm:
Spatial Predictive Modelling.
It aims to introduce some novel, accurate, hybrid geostatistical and machine
learning methods for spatial predictive modelling. It currently contains two
commonly used
Thank you very much, Roger! The suggestions are very helpful.
Best wishes,
Jin
-Original Message-
From: Roger Bivand [mailto:roger.biv...@nhh.no]
Sent: Friday, 25 November 2016 7:24 PM
To: Li Jin
Cc: r-sig-geo@r-project.org
Subject: Re: [R-sig-Geo] Error with loading rJava for spcosa
Hi All,
I have been using library(spcosa) in R version 3.2.3 (2015-12-10) and all
worked well, until today.
The error was as below when I called:
> library(spcosa)
Loading required package: rJava
Error : .onLoad failed in loadNamespace() for 'rJava', details:
call: fun(libname, pkgname)
Hi All,
I need to use the predict{gstat} function in one of my functions for a R
package. I use RStudio to make the package. When I specified gstat::predict in
the function, I received the following error:
Error: 'predict' is not an exported object from 'namespace:gstat'
The session
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