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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 <tt>bio-statsample-timeseries</tt> provides module Statsample::TimeSeries with support for time series, including ARIMA estimation using Kalman-Filter. 
* Gem <tt>statsample-sem</tt> provides a DSL to R libraries +sem+ and +OpenMx+
* Gem <tt>statsample-glm</tt> provides you with GML method, to work with Logistic, Poisson and Gaussian regression ,using ML or IRWLS. 
* Close integration with gem <tt>reportbuilder</tt>, 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 <tt>gem install rb-gsl</tt>.

*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 <tt>--platform ruby</tt>

```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.

statsample

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