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I'm pretty new in R, and I couldn't find any information about a package who can do the following: supposing that I have a set of data (for instance, different text documents), which can have several classes.
For example, a datum could be a Sport, a Sport with Ball, a Sport without Ball and a Car. I'd like to be able to predict to which category the data belongs, considering that I may not hit that the datum is a Sport with Ball, but I'd be happy if I correctly predict that it's a Sport.
Which package can provide this kind of stuff? Some examples would be useful, if possible.
Thanks in advance
I am not aware of any specific packages in R that can do hierarchical classification. So there are two options:
Use the C API SVMstruct http://www.cs.cornell.edu/people/tj/svm_light/svm_struct.html. Programming this in R from scratch will be quite some work.
Build your own hierarchical classifier system. In the top-down case you will have a multi-class classifier for each level, e.g. rec vs sci and motorcycles vs sport etc. You will use the top classifier and use its prediction to choose next classifier. The data you feed in to train a classifier with a node is the union of all data in the subtree rooted at that node.
For details read e.g. http://jmlr.org/papers/v6/tsochantaridis05a.html
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Is there any package that you would recommend which can be used to calculate the precision, F1, recall for multi class classification task in R. I tried to use ROCR but it states that:
ROCR currently supports only evaluation of binary classification tasks
I know that you were looking for a solution in R. That said, this is a link to a nice solution library in Python, using scikit-learn version 0.14. Python is very similar to R in a lot of respects (if you haven't used it before), and this could be a good place to start.
Another place you might want to look, if you are focused on R, is the the PerfMeas package. As I quote, this "Package implements different performance measures for
classification and ranking tasks. AUC, precision at a given recall, F-score for single and multiple classes are available."
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This is a borderline question for Stackoverflow, I know, but I am looking for a package. If I can't get an answer here I will transfer to https://stats.stackexchange.com/. I am looking for a R package or a method to create a phase diagram. This means I have e.g. two variables, like air pressure and temperature, and a binary variable (to make it easier) indicating if the substance is liquid or frozen. Below you find a typical example of a phase diagram. I need to estimate the transition borders or something however just in a case with two groups. Every hint is appreciated.
I think about the closet you will get is function diagram in package CHNOSZ. There's a lot to read about in this package and it has some nice vignettes. But, it seems to calculate phase diagrams from first principles or theory. Perhaps if you look at the code for diagram you can figure out a fairly easy way to use your empirical data.
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I am looking for a free software for mathematical modeling.
Here is a list of things I might be willing to achieve with this software: Integrating functions, solving differential equations, graph theory, analyzing infinite series, local stability analysis, Taylor series, get eigenvectors, compute the long term behaviour of a system of equations, etc...
Here is a related SE post. I am surprised that nobody is suggesting R. I am currently a R user and already use R for graph theory. Therefore I would appreciate to use R also to make other mathematical modeling. Is R less efficient that Sage, SimPy, Mathematica and others for mathematical modeling? Why? Do you know a manual providing exaplanation for how to make mathematical modeling with R?
Thank you
Sounds like R is your first way to go. It does not make to good sense to compare R with any other tool in such a braod way you are asking for. R packages differ largely in efficiency, some are in fact C tools while others are written in the R language. As a start R can hardly be any wrong and is free.
Matlab might be a stable alternative, Julia is rising but still pre alpha.
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I'm doing a study on time series data of protein phosphorylation events, and I want to use dynamic bayesian network to learn the network structure. I found your Bayes net toolbox can be helpful for my study.But I'm more familiar with R. Is there any R packages equivalent to Matlab's Bayes net toolbox, which can learn the network structure using time series data? Thank you!
try the following R packages to perform dynamic bayesian networks inference:
http://cran.r-project.org/web/packages/G1DBN/index.html
and
http://cran.r-project.org/web/packages/ebdbNet/index.html
enjoy!
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I'm looking for a simple MCMC Bayesian network Inference function/package in R. Essentially, I just want a function that accepts the matrix containing my samples x my variables ( + optional parameters like burn-in and iteration counts) and returns the adjacency matrix of the inferred network.
I had been using the Matlab toolkit "BayesNet", which offers a simple 'learn_struct_mcmc' function which offers most of what I'm looking for. I'm looking for an equivalent in R.
I've been looking through the packages in http://cran.r-project.org/web/views/Bayesian.html, but haven't seen anything that quite does what I'm looking for. I wasn't trained as a statistician, and many of the packages I've looked at on that list either lack documentation or have more complicated statistics than I'm comfortable wiring together myself. I just need a simple function with "reasonable" defaults to get started.
Bonus points for something that leverages Rmpi or snow.
This gave me 132 possible relevant functions.
library(sos)
findFn("bayesian network")
How about this package.
http://cran.r-project.org/web/packages/MCMCpack/index.html
The closest thing to what I had in mind that I've found is the hc() function in the blearn package. They have a variety of other Bayesian network inference functions, as well, some of which can use snow.