Interesting comparison table and comments, regarding the following statistical packages: R, MATLAB, SAS, STATA and SPSS. I wish Statistica would be included. The table tells you which statistical methods are available in each package. The list of statistical methods is itself impressive. Note that Jackknife (a resampling method in the table below) has nothing to do with Jackknife regression (new technique not implemented in any package yet, though Dr. Granville has promised to provide the source code).
Also statistical libraries are available in most programming languages, for instance Pandas in Python. Here are five interesting articles:
The table (below) and additional information about the various packages can be found here.
TYPE OF STATISTICAL ANALYSIS | R | MATLAB | SAS | STATA | SPSS |
Nonparametric Tests | Yes | Yes | Yes | Yes | Yes |
T-test | Yes | Yes | Yes | Yes | Yes |
ANOVA & MANOVA | Yes | Yes | Yes | Yes | Yes |
ANCOVA & MANCOVA | Yes | Yes | Yes | Yes | Yes |
Linear Regression | Yes | Yes | Yes | Yes | Yes |
Generalized Least Squares | Yes | Yes | Yes | Yes | Yes |
Ridge Regression | Yes | Yes | Yes | ||
Lasso | Yes | Yes | Yes | ||
Generalized Linear Models | Yes | Yes | Yes | Yes | Yes |
Mixed Effects Models | Yes | Yes | Yes | Yes | Yes |
Logistic Regression | Yes | Yes | Yes | Yes | Yes |
Nonlinear Regression | Yes | Yes | Yes | ||
Discriminant Analysis | Yes | Yes | Yes | Yes | Yes |
Nearest Neighbor | Yes | Yes | Yes | Yes | |
Factor & Principal Components Analysis | Yes | Yes | Yes | Yes | Yes |
Copula Models | Yes | Yes | Experimental | ||
Cross-Validation | Yes | Yes | Yes | ||
Bayesian Statistics | Yes | Yes | Limited | ||
Monte Carlo, Classic Methods | Yes | Yes | Yes | Yes | Limited |
Markov Chain Monte Carlo | Yes | Yes | Yes | ||
Bootstrap & Jackknife | Yes | Yes | Yes | Yes | |
EM Algorithm | Yes | Yes | Yes | ||
Missing Data Imputation | Yes | Yes | Yes | Yes | Yes |
Outlier Diagnostics | Yes | Yes | Yes | Yes | Yes |
Robust Estimation | Yes | Yes | Yes | Yes | |
Longitudinal (Panel) Data | Yes | Yes | Yes | Yes | Limited |
Survival Analysis | Yes | Yes | Yes | Yes | Yes |
Path Analysis | Yes | Yes | Yes | ||
Propensity Score Matching | Yes | Yes | Limited | Limited | |
Stratified Samples (Survey Data) | Yes | Yes | Yes | Yes | Yes |
Experimental Design | Yes | Yes | |||
Quality Control | Yes | Yes | Yes | Yes | |
Reliability Theory | Yes | Yes | Yes | Yes | Yes |
Univariate Time Series | Yes | Yes | Yes | Yes | Limited |
Multivariate Time Series | Yes | Yes | Yes | Yes | |
Markov Chains | Yes | Yes | |||
Hidden Markov Models | Yes | Yes | |||
Stochastic Volatility Models | Yes | Yes | Limited | Limited | Limited |
Diffusions | Yes | Yes | |||
Counting Processes | Yes | Yes | Yes | ||
Filtering | Yes | Yes | Limited | Limited | |
Instrumental Variables | Yes | Yes | Yes | Yes | |
Simultaneous Equations | Yes | Yes | Yes | Yes | |
Splines | Yes | Yes | Yes | Yes | |
Nonparametric Smoothing Methods | Yes | Yes | Yes | Yes | |
Extreme Value Theory | Yes | Yes | |||
Variance Stabilization | Yes | Yes | |||
Cluster Analysis | Yes | Yes | Yes | Yes | Yes |
Neural Networks | Yes | Yes | Yes | Limited | |
Classification & Regression Trees | Yes | Yes | Yes | Limited | |
Boosting Classification & Regression Trees | Yes | Yes | |||
Random Forests | Yes | Yes | |||
Support Vector Machines | Yes | Yes | Yes | ||
Signal Processing | Yes | Yes | |||
Wavelet Analysis | Yes | Yes | Yes | ||
ROC Curves | Yes | Yes | Yes | Yes | Yes |
Optimization | Yes | Yes | Yes | Limited |
Comment
Dear Dr. Granville, the Stata / SPSS implementation of non-linear regression is quite inflexible. The SPSS implementation is especially bad, allowing one to type only simple, non-recursive functions in a pop-up window. If you got a project about implementing a non-linear regression for a complex functional form, you would use R, Matlab or a similar programming language. Following the general vibe of responses, I changed the “Non-linear Regression / SPSS” field to “Limited” to avoid potential misinterpretations of the table. However, the truth is: the SPSS implementation of non-linear regression is unsatisfactory for most industry-level research.
Lasso is available in SPSS only as part of categorical regression, which does not cover linear regression and generalized linear models. So in 90% of real-life situations lasso is not there… Regarding AMOS, it is not part of the standard SPSS license and IBM is charging extra money for it. But I can see how one can make an argument that buying SPSS and AMOS together is still cheaper than buying the standard SAS portfolio. So I have updated the web-site accordingly. Thank you for your comments.
On a different note, I have updated the table recently to include recent advances, like attempts of the SAS Institute to address Boosting and Random Forests…. Any further feedback is appreciated.
R does everything and is free. I have used Matlab, SAS and SPSS. Matlab and SAS are very good, but the biggest problem is that I can't install a version on my home computer freely. Also, if I want something beyond the license that my company has purchased, then I have to go through a process to build a business case to get that "package". If I want that for R, I just go to CRAN and download it. The costs for R have mainly been books to learn R. However, there are so many free resources on R that you can learn to do it without buying anything.
Hello Vince G.,
Yes, it a well taken response, since it was a comparison of various software as remarked,
Yes, it is tricky comparison-- I must admit. I am not heavy IT / CS but use all software to my advantage and in the proper context and use it for a good application -- proven over a long haul -- On just another note that old style statisticians are die-hard. Thanks to all FRS and ASA and ISI pioneers.. from Fischer to Box , From H. Cramer to Sir C.R/ Rao, from Shewhart to Deming, their contributions are invaluable -- Big Data or not .. withstanding.
speaking on Gartner -- I religious follow theirs and see latest comparison for the BASEL compliance areas, I am looking forward to your take on that quadrant posting of various Software vendors.
Thanks for your time,
**I just merely observed. In fact, I have not been in SAS for a bit.. Thx.
Hi Chandrasekhara - I've been using SAS for many years and it satisfied my needs. It does not matter the number of functions a package offers, only whether it offers what you are likely to use, and if it does it well. Here's an article on how to select a statistical package.
On a higher level, producing software reviews and comparisons is very tricky. Your reviews get outdated very quickly, and easily invite heavy criticism. Gartner sometimes provide interesting comparisons. I'm glad we have members here filling the gaps found in these reports.
Mathematica should be included here. Yes, I know it's proprietary, but one reason I pay for it (and use it) is the high degree of integration - a kind of almost everything is a "first-class object" notion. This makes using the statistical stuff they have easier and more productive. Regardless, I like seeing attempts at package evaluations. A future evaluation would be include some notion of scalability. I find myself doing ever bigger problems on my laptop; one reason is that my current laptop is much more powerful than past versions.
Please let us know if SAS does not have Experimental Designs and Quality Control .. Is not .JMP a part of SAS ?? Please confirm .. Thanks
THE above table implies (???) SAS does not have non-parametric Tests. Please confirm if the YES is off the column to the right ??
A reader said that this table has many errors about SPSS: Just starting at the top, contrary to what is indicated, SPSS has - and has had for years - ridge regression, lasso, nonlinear regression, path analysis (Amos) and more.
© 2020 Data Science Central ® Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service
Most Popular Content on DSC
To not miss this type of content in the future, subscribe to our newsletter.
Other popular resources
Archives: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More
Most popular articles
You need to be a member of Data Science Central to add comments!
Join Data Science Central