The question says it all. I am wondering whether there is an implementation of this specific regionalization algorithm in R. I found implementations of the SKATER and REDCAP algorithms but not the max-p region model.
I found one in using rgeoda - an R package GeoDa
Related
I'm currently dealing with multivariate dynamic time warping (DTW) in R. The best library I found so far is the dtw package as described here: http://dtw.r-forge.r-project.org/
But I do not know how multivariate dtw is actually implemented and it is also not described in the description of the package. All in all, I would like to know if it implements DTWD (Dependent DTW) or DTWI (Independent DTW).
Does anyone have an idea or a suggestion how to find out which of these two approaches the package uses? Or are there libaries which allow me to choose the variant?
You can always look at the companion papers that come with the packages for a more theoretical grounding of the packages
https://www.jstatsoft.org/article/view/v031i07
Additionally, you can always look into the "guts" of the functions to understand how things are implemented in the code
https://www.rdocumentation.org/packages/dtw/versions/1.20-1/source
As far as this question in general, perhaps it is better suited for https://stats.stackexchange.com/ where the questions are more methodological than programming.
The two types of multivariate dynamic time warping are carefully explained here
https://www.cs.ucr.edu/~eamonn/Multi-Dimensional_DTW_Journal.pdf
I'm looking to set up SVM RFE and found the SO article here: Implementation of SVM-RFE Algorithm in R
It appears that the SVM function in the code is different from that in the e1071 library and i've been looking around to find the library with the function referenced in the above article, with no luck. The parameters in the example linked above appear different from what's used the e1071 library and i'm hoping someone out there might have some insight. I've been looking around a lot without any luck in tracking down the difference between "SVM()" in the example and "svm()" from e1071.
I am looking for ways to determine number of optimal factors in R factanal function. The most used method (conduct a pca and use scree plot to determine the number of factors) is already known to me. I have found a method described here to be easier for non technical folks like me. Unfortunately the R script is no longer accessible in which the method was implemented. I was wondering if there is a package available in R that does the same?
The method was originally proposed in this study: Determining the number of factors to retain in an exploratory factor analysis using comparison data of known factorial structure.
The R code is now moved here as per the author.
EFA.dimensions ist also a nice and easy to use package for that
I am working on a small project in survival analysis and making use of the paper:
Lagakos, De Gruttolla - Analysis of Doubly Censored Data, with applications to AIDS.
Before implementing their code in R, I wanted to know if there were already packages that implement the method they describe. I am aware of the package dblcens however, it's functions use an EM algorithm rather than the algorithm described by Lagakos and De Gruttolla.
Has this algorithm already been implemented in some R package?
Thank you.
I'm checking a simple moving average crossing strategy in R. Instead of running a huge simulation over the 2 dimenional parameter space (length of short term moving average, length of long term moving average), I'd like to implement the Particle Swarm Optimization algorithm to find the optimal parameter values. I've been browsing through the web and was reading that this algorithm was very effective. Moreover, the way the algorithm works fascinates me...
Does anybody of you guys have experience with implementing this algorithm in R? Are there useful packages that can be used?
Thanks a lot for your comments.
Martin
Well, there is a package available on CRAN called pso, and indeed it is a particle swarm optimizer (PSO).
I recommend this package.
It is under actively development (last update 22 Sep 2010) and is consistent with the reference implementation for PSO. In addition, the package includes functions for diagnostics and plotting results.
It certainly appears to be a sophisticated package yet the main function interface (the function psoptim) is straightforward--just pass in a few parameters that describe your problem domain, and a cost function.
More precisely, the key arguments to pass in when you call psoptim:
dimensions of the problem, as a vector
(par);
lower and upper bounds for each
variable (lower, upper); and
a cost function (fn)
There are other parameters in the psoptim method signature; those are generally related to convergence criteria and the like).
Are there any other PSO implementations in R?
There is an R Package called ppso for (parallel PSO). It is available on R-Forge. I do not know anything about this package; i have downloaded it and skimmed the documentation, but that's it.
Beyond those two, none that i am aware of. About three months ago, I looked for R implementations of the more popular meta-heuristics. This is the only pso implementation i am aware of. The R bindings to the Gnu Scientific Library GSL) has a simulated annealing algorithm, but none of the biologically inspired meta-heuristics.
The other place to look is of course the CRAN Task View for Optimization. I did not find another PSO implementation other than what i've recited here, though there are quite a few packages listed there and most of them i did not check other than looking at the name and one-sentence summary.