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I'll post a couple of useful codes on the subject tomorrow.
# perform ANOVA on one or more trained models anova(object, ...) that easy =)
By the way, if there are any people who know R, a beginner's question. I see that there are several R distributions, R-server, some "A web application framework for R" http://shiny.rstudio.com/ , monster packages from Microsoft... What to choose?
I haven't used it from MS, I can't say.
Men!
If you have the slightest prerequisite - programming experience in any language and some knowledge of statistics, then only R, and only R.
Matlab cannot be compared at all - it is a different package, and a paid package for a lot of money.
R's competitors are SAS and SPSS, but they are paid packages and R is beginning to overtake them. For 5 years Matlab was still being compared with R, but I don't see it in the last reviews anymore - it's gone into oblivion.
Nowadays R is the standard for statistics, there are a huge number of publications and in general a very powerful movement.
For example a very useful blog, published every day, you can subscribe for news: http://www.r-bloggers.com/
Here's a bunch of books for very reasonable money: http://www.twirpx.com/search/?query=R. Typed in a search for R. It searches well on keywords.
Let's not forget that R, as an algorithmic programming language, is one of the top ten languages and ranks next to the C variants.
To use it, you must take usual R with RStudio. Besides, let us not forget that the paid variant of R was bought by Microsoft and starts to promote its variant - follow the developments.
Men!
If you have the slightest prerequisite - programming experience in any language and some knowledge of statistics, then only R, and only R.
Matlab cannot be compared at all - it is a different package, and a paid package for a lot of money.
R's competitors are SAS and SPSS, but they are paid packages and R is beginning to overtake them. For 5 years Matlab was still being compared with R, but I don't see it in the last reviews anymore - it's gone into oblivion.
Nowadays R is the standard for statistics, there are a huge number of publications and in general a very powerful movement.
For example a very useful blog, published every day, you can subscribe to news: http://www.r-bloggers.com/
Here's a bunch of books for very reasonable money: http://www.twirpx.com/search/?query=R. Typed in a search for R. It searches well on keywords.
Let's not forget that R, as an algorithmic programming language, is one of the top ten languages and ranks next to the C variants.
To use it, you must take usual R with RStudio. Besides let's not forget that the paid R variant was bought by Microsoft and is beginning to promote it.
Well, it's the first day I'm slowly learning R, answer my questions, I want to compare R and Matlab features. But without any hullabaloo, in a balanced and calm manner :).
Great, it's my first day of learning R, answer my questions please, I want to compare possibilities of R and Matlab. Only without any chattering, in a balanced and calm way :).
Yes and yes. My colleague is clinging to MS SQL.
Signals: https://cran.r-project.org/web/packages/signal/index.html
There are probably other similar packages as well.
R grew out of S. It was originally developed for statistical data processing. Probably, some features of full-fledged languages may be missing in it, but it is convenient to do statistical research in it. And there are many (thousands) open-source packages for data processing and analysis.
Even the latest trends in machine learning - deep learning and the sensational xGBoost- have now been implemented.
Yes and yes. A colleague of mine is clinging to MS SQL.
Signals: https://cran.r-project.org/web/packages/signal/index.html
There are probably other similar packages as well.
R grew out of S. It was originally developed for statistical data processing. Probably, some features of full-fledged languages may be missing in it, but it is convenient to do statistical research in it. And there are many (thousands) open-source packages for data processing and analysis.
Even the latest trends in machine learning - deep learning and the sensational xGBoost - have now been implemented.