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Project Summary

This package allows to process files and databases for statistical purposes, with focus on estimation of parameters for several types of samples (simple random, stratified and multistage sampling).

Features:

Multiple Regression. Listwise analysis optimized with use of Alglib library. Pairwise analysis is executed on pure ruby and reports same values as SPSS Dominance Analysis. Based on Bodescu and Azen papers, DominanceAnalysis class can report dominance analysis for a sample and DominanceAnalysis. Bootstrap can execute bootstrap analysis to determine dominance stability, as recomended by Azen & Bodescu (2003). Classes for Vector, Datasets (set of Vectors) and Multisets (multiple datasets with same fields and type of vectors), and multiple methods to manipulate them Module Codification, to help to codify open questions Converters to and from database, csv and Excel files, and to output Mx and GGobi files Module Correlation provides covariance and pearson, spearman, point biserial, tau a, tau b and gamma correlations. Include methods to create correlation and covariance matrices. Module Crosstab provides function to create crosstab for categorical data Module HtmlReport provides methods to create a report for every class. Regression module provides linear regression methods Reliability analysis provides functions to analyze scales. Class ItemAnalysis provides statistics like mean, standard deviation for a scale, alpha and standarized alpha, and for each item: mean, correlation with total scale, mean if deleted, alpha is deleted. With HtmlReport, graph the histogram of the scale and the Item Characteristic Curve for each item Module SRS (Simple Random Sampling) provides a lot of functions to estimate standard error for several type of samples Interfaces to gdchart, gnuplot and SVG::Graph

ExamplesReal work session

# Read a CSV file, using '' and 'error' as missing values and ommiting 1 lines
ds=RubySS::CSV.read('resultados_c1.csv',['','error'],1)

# Create a new vector (column), calculating the mean of 13 vectors. Accept 1 missing values on one of the vectors

indice_constructivismo_becker=ds.vector_mean(%w{fd_2_1 fd_2_2 fd_3_1 fd_3_2 fd_3_3},1)

# Add the vector to the dataset

ds.add_vector("ind_cons_becker",indice_constructivismo_becker)

# Verify data. Vecto 'de_3_sex' must have values 'a' or 'b'. Dataset#verify returns and array with all errors

t_sex=create_test("Sex must be a o b",'de_3_sex') {|v| v['de_3_sex']=="a" or v['de_3_sex']=="b")}

p ds.verify(t_sexo)

# Creates a new dataset, based on the names of vectors

ds_software=ds.dup(%w{pe1n1 pe1n2 pe1n3 pe1n4 pe1n5 })

# Creates an html report, add a correlation matrix with all the scale vectors and save the report into a file
hr=RubySS::HtmlReport.new(ds_software,"correlations")
hr.add_correlation_matrix()
hr.save("correlation_matrix.html")

# Saves the new dataset
RubySS::CSV.write(ds_software,"ds_software.csv",true) Simulation of sample distribution for SRS

require File.dirname(__FILE__)+"/../lib/rubyss"
require 'rubyss/srs'
require 'rubyss/multiset'
require 'gnuplot'

tests=10000
sample_size=100

a=[]
(-20..+20).to_a.each {|i|
z=i/10.0
a+=[GSL::Ran.ugaussian_pdf(z)*1000]*25
}

pop=a.to_vector(:scale)
s=pop.standard_deviation_population
puts "Parameters:"
puts "Mean:"+pop.mean.to_s
puts "SD:"+s.to_s
puts "SE with replacement:"+RubySS::SRS.standard_error_ksd_wr(s, sample_size, pop.size).to_s
puts "SE without replacement:"+RubySS::SRS.standard_error_ksd_wor(s, sample_size,pop.size).to_s

sd_with=[]
sd_without=[]
monte_with=RubySS::Resample.repeat_and_save(tests) {
sample= pop.sample_with_replacement(sample_size)
sd_with.push(RubySS::SRS.standard_error_esd_wr(sample.sds,sample_size,pop.size))
sample.mean
}

monte_without=RubySS::Resample.repeat_and_save(tests) {
sample= pop.sample_without_replacement(sample_size)
sd_without.push(RubySS::SRS.standard_error_esd_wor(sample.sds,sample_size,pop.size))
sample.mean
}

v_sd_with=sd_with.to_vector(:scale)
v_sd_without=sd_without.to_vector(:scale)

v_with=monte_with.to_vector(:scale)
v_without=monte_without.to_vector(:scale)
puts "=============="
puts "Sample distribution - with Replacement"
puts "Mean:"+v_with.mean.to_s
puts "Sd:"+v_with.sds.to_s
puts "Sd (estimated):"+v_sd_with.mean.to_s
puts "Sample distribution - without Replacement"
puts "Mean:"+v_without.mean.to_s
puts "Sd:"+v_without.sds.to_s
puts "Sd (estimated):"+v_sd_without.mean.to_s

Tags

ruby statistics

In a Nutshell, ruby-statsample...

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