= Statsample

http://ruby-statsample.rubyforge.org/


== DESCRIPTION:

A suite for your basic and advanced statistics needs. Descriptive statistics, multiple regression, dominance analysis, scale's reliability analysis, bivariate statistics and others procedures.

== FEATURES:

* Multiple Regression. Listwise analysis optimized with use of Alglib library. Pairwise analysis is executed on pure ruby and reports same values as SPSS
* Dominance Analysis. Based on Bodescu and Azen papers, DominanceAnalysis class can report dominance analysis for a sample and DominanceAnalysisBootstrap can execute bootstrap analysis to determine dominance stability, as recomended by Azen & Bodescu (2003). 
* Classes for Vector, Datasets (set of Vectors) and Multisets (multiple datasets with same fields and type of vectors), and multiple methods to manipulate them
* Module Codification, to help to codify open questions
* Converters to and from database and csv files, and to output Mx and GGobi files
* Module Correlation provides covariance and pearson, spearman, point biserial, tau a, tau b and gamma correlations. Include methods to create correlation and covariance matrices
* Module Crosstab provides function to create crosstab for categorical data
* Module HtmlReport provides methods to create a report for scale analysis and matrix correlation
* Regression module provides linear regression methods
* Reliability analysis provides functions to analyze scales. Class ItemAnalysis provides statistics like mean, standard deviation for a scale, alpha and standarized alpha, and for each item: mean, correlation with total scale, mean if deleted, alpha is deleted. With HtmlReport, graph the histogram of the scale and the Item Characteristic Curve for each item
* Module SRS (Simple Random Sampling) provides a lot of functions to estimate standard error for several type of samples
* Interfaces to gdchart, gnuplot and SVG::Graph 


== Example of use:

    # Read a CSV file, using '' and 'error' as missing values and ommiting 1 lines
    ds=Statsample::CSV.read('resultados_c1.csv',['','error'],1)
    
    # Create a new vector (column), calculating the mean of 13 vectors. Accept 1 missing values on one of the vectors
    
    indice_constructivismo_becker=ds.vector_mean(%w{fd_2_1 fd_2_2 fd_3_1 fd_3_2 fd_3_3},1)
    
    # Add the vector to the dataset
    
    ds.add_vector("ind_cons_becker",indice_constructivismo_becker)
    
    # Verify data. Vecto 'de_3_sex' must have values 'a' or 'b'. Dataset#verify returns and array with all errors
    
    t_sex=create_test("Sex must be a o b",'de_3_sex') {|v| v['de_3_sex']=="a" or v['de_3_sex']=="b")}
    
    p ds.verify(t_sexo)
    
    
    # Creates a new dataset, based on the names of vectors
    
    ds_software=ds.dup(%w{pe1n1 pe1n2 pe1n3 pe1n4 pe1n5 })
    
    # Creates an html report, add a correlation matrix with all the scale vectors and save the report into a file 
    hr=Statsample::HtmlReport.new(ds_software,"correlations")
    hr.add_correlation_matrix()
    hr.save("correlation_matrix.html")
    
    
    # Saves the new dataset
    Statsample::CSV.write(ds_software,"ds_software.csv",true)

== REQUIREMENTS:

Optional: 

* Plotting: gnuplot and rbgnuplot, SVG::Graph
* Advanced Statistical: gsl and rb-gsl (http://rb-gsl.rubyforge.org/)

== DOWNLOAD
* Gems and bugs report: http://rubyforge.org/projects/ruby-statsample/
* SVN and Wiki: http://code.google.com/p/ruby-statsample/

== INSTALL:

  sudo gem install ruby-statsample

For optimization on *nix env

  sudo gem install ruby-statsample-optimization

Available setup.rb file

  sudo gem ruby setup.rb

== LICENSE:

GPL-2 (See LICENSE.txt)