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https://github.com/clbustos/statsample

A suite for basic and advanced statistics on Ruby.
https://github.com/clbustos/statsample

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A suite for basic and advanced statistics on Ruby.

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# Statsample



Homepage :: https://github.com/sciruby/statsample



[![Build Status](https://travis-ci.org/clbustos/statsample.svg?branch=master)](https://travis-ci.org/clbustos/statsample)

[![Gem Version](https://badge.fury.io/rb/statsample.svg)](http://badge.fury.io/rb/statsample)

## DESCRIPTION



A suite for basic and advanced statistics on Ruby. Tested on Ruby 2.1.1p76 (June 2014), 1.8.7, 1.9.1, 1.9.2 (April, 2010), ruby-head(June, 2011) and JRuby 1.4 (Ruby 1.8.7 compatible).



Include:

* Descriptive statistics: frequencies, median, mean, standard error, skew, kurtosis (and many others).

* Imports and exports datasets from and to Excel, CSV and plain text files.

* Correlations: Pearson's r, Spearman's rank correlation (rho), point biserial, tau a, tau b and  gamma.  Tetrachoric and Polychoric correlation provides by +statsample-bivariate-extension+ gem.

* Intra-class correlation

* Anova: generic and vector-based One-way ANOVA and Two-way ANOVA, with contrasts for One-way ANOVA.

* Tests: F, T, Levene, U-Mannwhitney.

* Regression: Simple, Multiple (OLS), Probit  and Logit

* Factorial Analysis: Extraction (PCA and Principal Axis), Rotation (Varimax, Equimax, Quartimax) and Parallel Analysis and Velicer's MAP test, for estimation of number of factors.

* Reliability analysis for simple scale and a DSL to easily analyze multiple scales using factor analysis and correlations, if you want it.

* Basic time series support

* Dominance Analysis, with multivariate dependent and bootstrap (Azen & Budescu)

* Sample calculation related formulas

* Structural Equation Modeling (SEM), using R libraries +sem+ and +OpenMx+

* Creates reports on text, html and rtf, using ReportBuilder gem

* Graphics: Histogram, Boxplot and Scatterplot



## Principles



* Software Design: 

  * One module/class for each type of analysis

  * Options can be set as hash on initialize() or as setters methods

  * Clean API for interactive sessions

  * summary() returns all necessary informacion for interactive sessions

  * All statistical data available though methods on objects

  * All (important) methods should be tested. Better with random data.

* Statistical Design

  * Results are tested against text results, SPSS and R outputs.

  * Go beyond Null Hiphotesis Testing, using confidence intervals and effect sizes when possible

  * (When possible) All references for methods are documented, providing sensible information on documentation 



## Features



* Classes for manipulation and storage of data:

  * Statsample::Vector: An extension of an array, with statistical methods like sum, mean and standard deviation

  * Statsample::Dataset: a group of Statsample::Vector, analog to a excel spreadsheet or a dataframe on R. The base of almost all operations on statsample. 

  * Statsample::Multiset: multiple datasets with same fields and type of vectors

* Anova module provides generic Statsample::Anova::OneWay and vector based Statsample::Anova::OneWayWithVectors. Also you can create contrast using Statsample::Anova::Contrast

* Module Statsample::Bivariate provides covariance and pearson, spearman, point biserial, tau a, tau b, gamma, tetrachoric (see Bivariate::Tetrachoric) and polychoric (see Bivariate::Polychoric) correlations. Include methods to create correlation and covariance matrices

* Multiple types of regression.

  * Simple Regression :  Statsample::Regression::Simple

  * Multiple Regression: Statsample::Regression::Multiple

  * Logit Regression:    Statsample::Regression::Binomial::Logit

  * Probit Regression:    Statsample::Regression::Binomial::Probit

* Factorial Analysis algorithms on Statsample::Factor module.

  * Classes for Extraction of factors: 

    * Statsample::Factor::PCA

    * Statsample::Factor::PrincipalAxis

  * Classes for Rotation of factors: 

    * Statsample::Factor::Varimax

    * Statsample::Factor::Equimax

    * Statsample::Factor::Quartimax

  * Classes for calculation of factors to retain

    * Statsample::Factor::ParallelAnalysis performs Horn's 'parallel analysis' to a principal components analysis to adjust for sample bias in the retention of components.

    * Statsample::Factor::MAP performs Velicer's Minimum Average Partial (MAP) test, which retain components as long as the variance in the correlation matrix represents systematic variance.

* Dominance Analysis. Based on Budescu and Azen papers, dominance analysis is a method to analyze the relative importance of one predictor relative to another on multiple regression

  * Statsample::DominanceAnalysis class can report dominance analysis for a sample, using uni or multivariate dependent variables

  * Statsample::DominanceAnalysis::Bootstrap can execute bootstrap analysis to determine dominance stability, as recomended by  Azen & Budescu (2003) link[http://psycnet.apa.org/journals/met/8/2/129/]. 

* Module Statsample::Codification, to help to codify open questions

* Converters to import and export data:

  * Statsample::Database : Can create sql to create tables, read and insert data

  * Statsample::CSV : Read and write CSV files

  * Statsample::Excel : Read and write Excel files

  * Statsample::Mx    : Write Mx Files

  * Statsample::GGobi : Write Ggobi files

* Module Statsample::Crosstab provides function to create crosstab for categorical data

* Module Statsample::Reliability provides functions to analyze scales with psychometric methods. 

  * Class Statsample::Reliability::ScaleAnalysis provides statistics like mean, standard deviation for a scale, Cronbach's alpha and standarized Cronbach's alpha, and for each item: mean, correlation with total scale, mean if deleted, Cronbach's alpha is deleted.

  * Class Statsample::Reliability::MultiScaleAnalysis provides a DSL to easily analyze reliability of multiple scales and retrieve correlation matrix and factor analysis of them.

  * Class Statsample::Reliability::ICC provides intra-class correlation, using Shrout & Fleiss(1979) and McGraw & Wong (1996) formulations.

* Module Statsample::SRS (Simple Random Sampling) provides a lot of functions to estimate standard error for several type of samples

* Module Statsample::Test provides several methods and classes to perform inferencial statistics

  * Statsample::Test::BartlettSphericity

  * Statsample::Test::ChiSquare

  * Statsample::Test::F

  * Statsample::Test::KolmogorovSmirnov (only D value)

  * Statsample::Test::Levene

  * Statsample::Test::UMannWhitney

  * Statsample::Test::T

  * Statsample::Test::WilcoxonSignedRank

* Module Graph provides several classes to create beautiful graphs using rubyvis

  * Statsample::Graph::Boxplot

  * Statsample::Graph::Histogram

  * Statsample::Graph::Scatterplot

* Gem bio-statsample-timeseries provides module Statsample::TimeSeries with support for time series, including ARIMA estimation using Kalman-Filter. 

* Gem statsample-sem provides a DSL to R libraries +sem+ and +OpenMx+

* Gem statsample-glm provides you with GML method, to work with Logistic, Poisson and Gaussian regression ,using ML or IRWLS. 

* Close integration with gem reportbuilder, to easily create reports on text, html and rtf formats.



# Examples of use:



See the [examples folder](https://github.com/clbustos/statsample/tree/master/examples/) too.



## Boxplot



```ruby

require 'statsample'



ss_analysis(Statsample::Graph::Boxplot) do 

  n=30

  a=rnorm(n-1,50,10)

  b=rnorm(n, 30,5)

  c=rnorm(n,5,1)

  a.push(2)

  boxplot(:vectors=>[a,b,c], :width=>300, :height=>300, :groups=>%w{first first second}, :minimum=>0)

end

Statsample::Analysis.run # Open svg file on *nix application defined

```



## Correlation matrix



```ruby

require 'statsample'

# Note R like generation of random gaussian variable

# and correlation matrix



ss_analysis("Statsample::Bivariate.correlation_matrix") do

  samples=1000

  ds=data_frame(

    'a'=>rnorm(samples), 

    'b'=>rnorm(samples),

    'c'=>rnorm(samples),

    'd'=>rnorm(samples))

  cm=cor(ds) 

  summary(cm)

end



Statsample::Analysis.run_batch # Echo output to console

```



# Requirements



Optional: 



* Plotting: gnuplot and rbgnuplot, SVG::Graph

* Factorial analysis and polychorical correlation(joint estimate and polychoric series): gsl library and rb-gsl (https://rubygems.org/gems/rb-gsl/). You should install it using gem install rb-gsl. 



*Note*: Use gsl 1.12.109 or later.



# Resources



* Source code on github :: http://github.com/clbustos/statsample

* Docs :: http://statsample.apsique.cl/

* Bug report and feature request :: http://github.com/clbustos/statsample/issues

* E-mailing list :: http://groups.google.com/group/statsample



# Installation



```bash

$ sudo gem install statsample

```



On *nix, you should install statsample-optimization to retrieve gems gsl, statistics2 and a C extension to speed some methods. 



There are available precompiled version for Ruby 1.9 on x86, x86_64 and mingw32 archs.



```bash

$ sudo gem install statsample-optimization

```



If you use Ruby 1.8, you should compile statsample-optimization, usign parameter --platform ruby



```bash

$ sudo gem install statsample-optimization --platform ruby

```



If you need to work on Structural Equation Modeling, you could see +statsample-sem+. You need R with +sem+ or +OpenMx+ [http://openmx.psyc.virginia.edu/] libraries installed



```bash

$ sudo gem install statsample-sem

```



Available setup.rb file



```bash

sudo gem ruby setup.rb

```



## License



BSD-3 (See LICENSE.txt)



Could change between version, without previous warning. If you want a specific license, just choose the version that you need.