From 34d1cb0708b278053fd4c61cb7a3c61a5c9553f3 Mon Sep 17 00:00:00 2001 From: Kelson Adams Date: Sun, 29 May 2016 20:57:48 -0700 Subject: [PATCH] Fix typos in readme --- README.md | 42 +++++++++++++++++++++--------------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/README.md b/README.md index 059d067..d232baf 100644 --- a/README.md +++ b/README.md @@ -27,23 +27,23 @@ Include: ## 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 - * summary() returns all necessary informacion for interactive sessions + * summary() returns all necessary information 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 + * Go beyond Null Hypothesis 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::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 @@ -53,10 +53,10 @@ 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 @@ -65,7 +65,7 @@ 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 recommended 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 @@ -74,12 +74,12 @@ 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. - * 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. +* 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 standardized 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 +* Module Statsample::Test provides several methods and classes to perform inferential statistics * Statsample::Test::BartlettSphericity * Statsample::Test::ChiSquare * Statsample::Test::F @@ -92,9 +92,9 @@ Include: * 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 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. +* 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. # Examples of use: @@ -106,7 +106,7 @@ See the [examples folder](https://github.com/clbustos/statsample/tree/master/exa ```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) @@ -121,17 +121,17 @@ Statsample::Analysis.run # Open svg file on *nix application defined ```ruby require 'statsample' -# Note R like generation of random gaussian variable +# 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 @@ -140,10 +140,10 @@ Statsample::Analysis.run_batch # Echo output to console # Requirements -Optional: +Optional: * Plotting: gnuplot and rbgnuplot, SVG::Graph -* Factorial analysis and polychorical correlation(joint estimate and polychoric series): gsl library and rb-gsl (https://rubygems.org/gems/rb-gsl/). You should install it using gem install rb-gsl. +* Factorial analysis and polychorical correlation(joint estimate and polychoric series): gsl library and rb-gsl (https://rubygems.org/gems/rb-gsl/). You should install it using gem install rb-gsl. *Note*: Use gsl 1.12.109 or later. @@ -160,7 +160,7 @@ Optional: $ 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. @@ -168,7 +168,7 @@ There are available precompiled version for Ruby 1.9 on x86, x86_64 and mingw32 $ sudo gem install statsample-optimization ``` -If you use Ruby 1.8, you should compile statsample-optimization, usign parameter --platform ruby +If you use Ruby 1.8, you should compile statsample-optimization, using parameter --platform ruby ```bash $ sudo gem install statsample-optimization --platform ruby