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Examining either continuous, binary, multinomial target variables or just numerical data, LogXact provides you with the latest methods of regression analysis. LogXact is particularly practical for the analysis of small data sets. Its user interface is easy to handle and the unsurpassed quality and variety of exact tests make this software an indispensable tool for thousands of statisticians in leading companies, government agencies and research institutes.
LogXact places a particular focus on the calculation of exact permutation-based tests. Therefore, the latest and fastest methods are implemented. In addition, the software offers specific methods to account for missing values in covariates.
In the latest version, you can also calculate logistic regression with Firth's PMLE procedure, run the model building by the best subset method and interpret the parameters using profile likelihood intervals.
Arguments for LogXact:
- Comprehensive package for regression analysis
- Easy to use interface
- Unsurpassed quality and variety of exact tests
- Particularly suitable for analyzing small data sets
- Integration of R code
LogXact is an exciting product. No other logistic regression software can match its ability to analyze small, sparse, or imbalanced binary data.
- It provides exact answers for those questionable studies in which you thought you had a reasonably large sample size, but unfortunately observed only a handful of responses.
- It performs both unconditional and conditional maximum likelihood inference for problems that are too difficult for the exact inference.
- Its user manual gives a detailed text-book-like exposition of the theory underlying unconditional maximum likelihood inference, conditional maximum likelihood inference, and conditional exact inference, all illustrated with numerous real examples. The manual is very convenient, with a large index and a detailed table of contents.
- Its friendly spreadsheet-like data editors bring you close to your data and facilitate model building. You can import ASCII, SAS, SPSS, SYSTAT and EGRET data sets directly into LogXact.
LogXact - Trial Version!
You can test the software for free with a 30-days trial version.
You can download the demoversion on the producer website by visiting the link below. You need to fill in a contract. After that the software will be delivered to your E-Mail address as a download.
|Operating System||Windows XP, Vista, 7|
|Min. CPU||486 Processor or higher|
|Min. RAM||16 MB RAM|
|Disk Space||10 MB|
New Features in LogXact 11
- Negative Binomial Regression: Regression for Count Data (in addition to Poisson Regression), Bias correction (Firth)
- Additional procedures for Bias Corrected (Firth) including Probit and ClogLog regression for binary data, Poisson regression for stratified and unstratified
- Profile Likelihood based Confidence Intervals for likelihood and penalized likelihood estimates
Features in LogXact 10
Windows® 7 and Vista support
Cytel works continuously to allow our customers a choice of supported operating systems. Cytel's StatXact® and LogXact® are now both approved for use in Windows® 7 and Windows® Vista.
Users can now use R scripts in conjunction with Cytel’s exact statistics software. Computers with R 2.3 or newer may run existing R files or write and run R programs with the R output displayed in LogXact®. R programs can also run analyses of either StatXact® or LogXact® datasets. All R outputs are displayed in the Cytel Studio interface (except R plots). In addition to R, Cytel continues support of its own batch language to for custom analysis and regression calculations automation.
Latest validated methods include
- Best subset selection in binary logistic regression
- Force inclusion of variables to the best subset
- Profile Likelihood Confidence Intervals for parameters of binary logistic regression
- Firth's correction to Profile Likelihood Confidence Intervals
See Penalized Maximum Likelihood Method (PMLE) example of Firth's correction, the procedure for obtaining the Profile Likelihood-based confidence intervals for the parameters with the Venzon and Moolgavkar algorithm (1988)).