Statsample¶ ↑

Homepage :: github.com/clbustos/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.

• 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

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

Examples of use:¶ ↑

See the examples folder too.

Boxplot¶ ↑

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¶ ↑

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 (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 :: github.com/clbustos/statsample

• Docs :: rubydoc.info/gems/statsample/

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

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

Installation¶ ↑

$ 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.

$ sudo gem install statsample-optimization

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

$ 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 [openmx.psyc.virginia.edu/] libraries installed

$ sudo gem install statsample-sem

Available setup.rb file

sudo gem ruby setup.rb

License¶ ↑

GPL-2 (See LICENSE.txt)