# Statsample
[](https://travis-ci.org/SciRuby/statsample)
[](https://codeclimate.com/github/SciRuby/statsample)
[](http://badge.fury.io/rb/statsample)
Homepage :: https://github.com/sciruby/statsample
# Installation
You should have a recent version of GSL and R (with the `irr` and `Rserve` libraries) installed. In Ubuntu:
```bash
$ sudo apt-get install libgs10-dev r-base r-base-dev
$ sudo Rscript -e "install.packages(c('Rserve', 'irr'))"
```
With these libraries in place, just install from rubygems:
```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.
```bash
$ [sudo] gem install statsample-optimization
```
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
```
# Documentation
You can see the latest documentation in [rubydoc.info](http://www.rubydoc.info/github/sciruby/statsample/master).
# Description
A suite for basic and advanced statistics on Ruby. Tested on CRuby 1.9.3, 2.0.0 and 2.1.1. See `.travis.yml` for more information.
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.
# Usage
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
```
# Resources
- Source code on github :: http://github.com/sciruby/statsample
- Bug report and feature request :: http://github.com/sciruby/statsample/issues
- E-mailing list :: https://groups.google.com/forum/#!forum/sciruby-dev
# 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.