CodeReview Round 1

README.txt → README.md

@@ -1,13 +1,16 @@
-= Statsample
+Statsample
+==========
 
-http://ruby-statsample.rubyforge.org/
+[http://ruby-statsample.rubyforge.org/](http://ruby-statsample.rubyforge.org/)
 
 
-== DESCRIPTION:
+DESCRIPTION:
+------------
 
 A suite for basic and advanced statistics on Ruby. Tested on Ruby 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.
@@ -24,9 +27,10 @@ Include:
 * Creates reports on text, html and rtf, using ReportBuilder gem
 * Graphics: Histogram, Boxplot and Scatterplot
 
-== PRINCIPLES
+PRINCIPLES
+----------
 
-* Software Design: 
+* 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
@@ -36,13 +40,14 @@ Include:
 * 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 
+  * (When possible) All references for methods are documented, providing sensible information on documentation
 
-== FEATURES:
+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::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
@@ -52,10 +57,10 @@ Include:
   * Logit Regression:    Statsample::Regression::Binomial::Logit
   * Probit Regression:    Statsample::Regression::Binomial::Probit
 * Factorial Analysis algorithms on Statsample::Factor module.
-  * Classes for Extraction of factors: 
+  * Classes for Extraction of factors:
     * Statsample::Factor::PCA
     * Statsample::Factor::PrincipalAxis
-  * Classes for Rotation of factors: 
+  * Classes for Rotation of factors:
     * Statsample::Factor::Varimax
     * Statsample::Factor::Equimax
     * Statsample::Factor::Quartimax
@@ -64,7 +69,7 @@ Include:
     * 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/]. 
+  * 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
@@ -73,15 +78,15 @@ Include:
   * 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. 
+* 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::F
   * Statsample::Test::KolmogorovSmirnov (only D value)
   * Statsample::Test::Levene
   * Statsample::Test::UMannWhitney
@@ -90,85 +95,91 @@ Include:
   * Statsample::Graph::Boxplot
   * Statsample::Graph::Histogram
   * Statsample::Graph::Scatterplot
-* Module Statsample::TimeSeries provides basic support for time series. 
+* Module Statsample::TimeSeries provides basic support for time series.
 * Gem +statsample-sem+ provides a DSL to R libraries +sem+ and +OpenMx+
-* Close integration with gem <tt>reportbuilder</tt>, to easily create reports on text, html and rtf formats.
-
-== Examples of use:
+* Close integration with gem `reportbuilder`, to easily create reports on text, html and rtf formats.
 
-See multiples examples of use on [http://github.com/clbustos/statsample/tree/master/examples/]
+Examples of use:
+--------------
 
-=== Boxplot
+See multiples examples of use on [http://github.com/clbustos/statsample/tree/master/examples/](http://github.com/clbustos/statsample/tree/master/examples/)
 
+Boxplot
+-------
+```ruby
     require 'statsample'
-    ss_analysis(Statsample::Graph::Boxplot) do 
+    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    
+    end
     Statsample::Analysis.run # Open svg file on *nix application defined
-
-=== Correlation matrix
-
+```
+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), 
+        'a'=>rnorm(samples),
         'b'=>rnorm(samples),
         'c'=>rnorm(samples),
         'd'=>rnorm(samples))
-      cm=cor(ds) 
+      cm=cor(ds)
       summary(cm)
     end
-
-    Statsample::Analysis.run_batch # Echo output to console
 
+    Statsample::Analysis.run_batch # Echo output to console
+```
 
-== REQUIREMENTS:
+REQUIREMENTS:
+-------------
 
-Optional: 
+Optional:
 
 * Plotting: gnuplot and rbgnuplot, SVG::Graph
-* Factorial analysis and polychorical correlation(joint estimate and polychoric series): gsl library and rb-gsl (http://rb-gsl.rubyforge.org/). You should install it using <tt>gem install gsl</tt>. 
-
-<b>Note</b>: Use gsl 1.12.109 or later.
+* Factorial analysis and polychorical correlation(joint estimate and polychoric series): gsl library and rb-gsl [http://rb-gsl.rubyforge.org/](http://rb-gsl.rubyforge.org/). You should install it using `gem install gsl`.
 
-== RESOURCES
+**Note**: Use gsl 1.12.109 or later.
 
-* Source code on github: http://github.com/clbustos/statsample
-* API: http://ruby-statsample.rubyforge.org/statsample/
-* Bug report and feature request: http://github.com/clbustos/statsample/issues
-* E-mailing list: http://groups.google.com/group/statsample
+RESOURCES:
+----------
 
-== INSTALL:
+* Source code on github: [http://github.com/clbustos/statsample](http://github.com/clbustos/statsample)
+* API: [http://ruby-statsample.rubyforge.org/statsample/](http://ruby-statsample.rubyforge.org/statsample/)
+* Bug report and feature request: [http://github.com/clbustos/statsample/issues](http://github.com/clbustos/statsample/issues)
+* E-mailing list: [http://groups.google.com/group/statsample](http://groups.google.com/group/statsample)
 
-  $ sudo gem install statsample
+INSTALL:
+---------
+  `$ sudo gem install statsample`
 
-On *nix, you should install statsample-optimization to retrieve gems gsl, statistics2 and a C extension to speed some methods. 
+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.
+There are available precompiled version for Ruby 1.9 on x86, x86\_64 and mingw32 archs.
 
-  $ sudo gem install statsample-optimization
+  `$ sudo gem install statsample-optimization`
 
-If you use Ruby 1.8, you should compile statsample-optimization, usign parameter <tt>--platform ruby</tt>
+If you use Ruby 1.8, you should compile statsample-optimization, usign parameter `--platform ruby`
 
-  $ sudo gem install statsample-optimization --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+ [http://openmx.psyc.virginia.edu/] libraries installed
+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/](http://openmx.psyc.virginia.edu/) libraries installed
 
-  $ sudo gem install statsample-sem
+  `$ sudo gem install statsample-sem`
 
 Available setup.rb file
 
-  sudo gem ruby setup.rb
+  `sudo gem ruby setup.rb`
 
-== LICENSE:
+LICENSE:
+-------
 
 GPL-2 (See LICENSE.txt)

features/acf.feature

@@ -0,0 +1,31 @@
+Feature: ACF
+
+  As a statistician
+  So that I can evaluate autocorrelation of a series
+  I want to evaluate acf
+
+Background: a timeseries
+
+  Given the following values in a timeseries:
+    | timeseries |
+    | 10  20  30  40  50  60  70  80  90  100 |
+    | 110 120 130 140 150 160 170 180 190 200 |
+
+Scenario: cross-check acf for 10 lags
+  When I provide 10 lags for acf
+  And I calculate acf
+  Then I should get 11 values in resultant acf
+  And I should see "1.0, 0.85, 0.7015037593984963, 0.556015037593985, 0.4150375939849624, 0.2800751879699248, 0.15263157894736842, 0.034210526315789476, -0.07368421052631578, -0.16954887218045114, -0.2518796992481203" as complete series 
+
+Scenario: cross-check acf for 5 lags
+  When I provide 5 lags for acf
+  And I calculate acf
+  Then I should get 6 values in resultant acf
+  And I should see "1.0, 0.85, 0.7015037593984963, 0.556015037593985, 0.4150375939849624, 0.2800751879699248" as complete series
+
+Scenario: first value should be 1.0
+  When I provide 2 lags for acf
+  And I calculate acf
+  Then I should get 3 values in resultant acf
+  And I should see 1.0 as first value
+

features/pacf.feature

@@ -0,0 +1,42 @@
+Feature: PACF
+
+  As a statistician
+  So that I can quickly evaluate partial autocorrelation of a series
+  I want to evaluate pacf
+
+Background: a timeseries
+
+  Given the following values in a timeseries:
+    | timeseries |
+    | 10  20  30  40  50  60  70  80  90  100 |
+    | 110 120 130 140 150 160 170 180 190 200 |
+
+Scenario: check pacf for 10 lags with unbiased
+  When I provide 10 lags for pacf
+  When I provide yw yule walker as method
+  Then I should get Array as resultant output
+  Then I should get 11 values in resultant pacf
+
+Scenario: check pacf for 5 lags with mle
+  When I provide 5 lags for pacf
+  When I provide mle yule walker as method
+  Then I should get Array as resultant output
+  Then I should get 6 values in resultant pacf
+
+Scenario: check first value of pacf
+  When I provide 5 lags for pacf
+  When I provide yw yule walker as method
+  Then I should get Array as resultant output
+  And I should see 1.0 as first value
+
+Scenario: check all values in pacf for 5 lags with mle
+  When I provide 5 lags for pacf
+  When I provide mle yule walker as method
+  Then I should get Array as resultant output
+  And I should see "1.0, 0.85, -0.07566212829370711, -0.07635069706072706, -0.07698628638512295, -0.07747034005560738" as complete series 
+
+Scenario: check all values in pacf for 5 lags with unbiased
+  When I provide 5 lags for pacf
+  When I provide yw yule walker as method
+  Then I should get Array as resultant output
+  And I should see "1.0, 0.8947368421052632, -0.10582010582010604, -0.11350188273265083, -0.12357534824820737, -0.13686534216335522" as complete series 

features/step_definitions.rb

@@ -0,0 +1,37 @@
+require 'statsample'
+include Statsample::TimeSeries
+
+Given /^the following values in a timeseries:$/ do |series|
+  arr = []
+  series.hashes.each do |sequence|
+    arr += sequence['timeseries'].split(' ').map(&:to_i).to_ts
+  end
+  @timeseries = arr.to_ts
+end
+
+When /^I provide (\d+) lags for p?acf$/ do |lags|
+  @lags = lags.to_i
+end
+
+When /^I provide (\w+) yule walker as method$/ do |method|
+  @method = method
+end
+
+Then /^I should get (\w+) as resultant output$/ do |klass|
+  @result = @timeseries.pacf(@lags, @method)
+  assert_equal @result.class.to_s, klass
+end
+
+Then /^I should get (\w+) values in resultant p?acf$/ do |values_count|
+  assert_equal @result.size, values_count.to_i
+end
+
+And /^I should see (\d+\.\d) as first value$/ do |first_value|
+  assert_equal @result.first, first_value.to_f
+end
+
+And /^I should see \"(.+)\" as complete series$/ do |series|
+  series = series.split(',').map(&:to_f)
+  assert_equal @result, series
+end
+

features/step_definitions_acf.rb

@@ -0,0 +1,9 @@
+require 'statsample'
+require 'debugger'
+include Statsample::TimeSeries
+
+#all instance variable and cucumber DSL s DRYed up in step_definitions.rb
+And /^I calculate acf$/ do
+  @result = @timeseries.acf(@lags)
+end
+

lib/statsample/analysis.rb

@@ -3,7 +3,7 @@
 
 module Statsample
   # DSL to create analysis without hazzle. 
-  # * Shortcuts methods to avoid use complete namescapes, many based on R  
+  # * Shortcuts methods to avoid use complete namespaces, many based on R  
   # * Attach/detach vectors to workspace, like R
   # == Example
   #  an1=Statsample::Analysis.store(:first) do

lib/statsample/arima.rb

@@ -0,0 +1,28 @@
+require 'debugger'
+module Statsample
+  module ARIMA
+    class ARIMA < Statsample::TimeSeries
+
+      def arima(ds, p, i, q)
+        if q.zero? 
+          self.ar(p)
+        elsif p.zero?
+          self.ma(p)
+        end
+      end
+
+      def ar(p)
+        #AutoRegressive part of model
+        #http://en.wikipedia.org/wiki/Autoregressive_model#Definition
+        #For finding parameters(to fit), we will use either Yule-walker
+        #or Burg's algorithm(more efficient)
+
+        degugger
+
+      end
+
+      def yule_walker()
+      end
+    end
+  end
+end