README.md in statsample-1.5.0 vs README.md in statsample-2.0.0

- old
+ new

@@ -30,22 +30,60 @@ 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 ``` +# Testing +See CONTRIBUTING for information on testing and contributing to statsample. + # Documentation You can see the latest documentation in [rubydoc.info](http://www.rubydoc.info/github/sciruby/statsample/master). +# Usage + +## Notebooks + +You can see some iruby notebooks here: + +### Statistics + +* [Correlation Matrix with daru and statsample](http://nbviewer.ipython.org/github/SciRuby/sciruby-notebooks/blob/master/Statistics/Correlation%20Matrix%20with%20daru%20and%20statsample.ipynb) +* [Dominance Analysis with statsample](http://nbviewer.ipython.org/github/SciRuby/sciruby-notebooks/blob/master/Statistics/Dominance%20Analysis%20with%20statsample.ipynb) +* [Reliability ICC](http://nbviewer.ipython.org/github/v0dro/sciruby-notebooks/blob/master/Statistics/Reliability%20ICC%20with%20statsample.ipynb) +* [Levene Test](http://nbviewer.ipython.org/github/v0dro/sciruby-notebooks/blob/master/Statistics/Levene%20Test.ipynb) +* [Multiple Regression](http://nbviewer.ipython.org/github/v0dro/sciruby-notebooks/blob/master/Statistics/Multiple%20Regression.ipynb) +* [Parallel Analysis on PCA](http://nbviewer.ipython.org/github/SciRuby/sciruby-notebooks/blob/master/Statistics/Parallel%20Analysis%20on%20PCA.ipynb) +* [Polychoric Analysis](http://nbviewer.ipython.org/github/SciRuby/sciruby-notebooks/blob/master/Statistics/Polychoric%20Correlation.ipynb) +* [Reliability Scale and Multiscale Analysis](https://github.com/SciRuby/sciruby-notebooks/blob/master/Statistics/Reliability%20Scale%20Analysis.ipynb) +* [Velicer MAP Test](http://nbviewer.ipython.org/github/SciRuby/sciruby-notebooks/blob/master/Statistics/Velicer%20MAP%20test.ipynb) + +### Visualizations + +* [Creating Boxplots with daru and statsample](http://nbviewer.ipython.org/github/SciRuby/sciruby-notebooks/blob/master/Visualization/Boxplot%20with%20daru%20and%20statsample.ipynb) +* [Creating A Histogram](http://nbviewer.ipython.org/github/SciRuby/sciruby-notebooks/blob/master/Visualization/Creating%20a%20Histogram.ipynb) +* [Creating a Scatterplot](http://nbviewer.ipython.org/github/SciRuby/sciruby-notebooks/blob/master/Visualization/Scatterplot%20with%20statsample.ipynb) + +### Working with DataFrame and Vector + +* [Creating Vectors and DataFrames with daru](http://nbviewer.ipython.org/github/SciRuby/sciruby-notebooks/blob/master/Data%20Analysis/Creation%20of%20Vector%20and%20DataFrame.ipynb) +* [Detailed Usage of Daru::Vector](http://nbviewer.ipython.org/github/SciRuby/sciruby-notebooks/blob/master/Data%20Analysis/Usage%20of%20Vector.ipynb) +* [Detailed Usage of Daru::DataFrame](http://nbviewer.ipython.org/github/SciRuby/sciruby-notebooks/blob/master/Data%20Analysis/Usage%20of%20DataFrame.ipynb) +* [Visualizing Data with Daru::DataFrame](http://nbviewer.ipython.org/github/SciRuby/sciruby-notebooks/blob/master/Visualization/Visualizing%20data%20with%20daru%20DataFrame.ipynb) + +## Examples + +See the /examples directory for some use cases. The notebooks listed above have mostly +the same examples, and they look better so you might want to see that first. + # 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 @@ -73,12 +111,11 @@ - (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. + - Uses [daru](https://github.com/v0dro/daru) for storing data and basic statistics. - 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 @@ -98,14 +135,11 @@ - 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 +- Converters to export data: - 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. @@ -127,55 +161,9 @@ - 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. - -# 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