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Fixed typos in readme #49

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42 changes: 21 additions & 21 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand All @@ -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
Expand All @@ -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
Expand All @@ -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
Expand All @@ -92,9 +92,9 @@ Include:
* Statsample::Graph::Boxplot
* Statsample::Graph::Histogram
* 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>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.
* 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.

# Examples of use:
Expand All @@ -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)
Expand All @@ -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

Expand All @@ -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 <tt>gem install rb-gsl</tt>.
* 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 <tt>gem install rb-gsl</tt>.

*Note*: Use gsl 1.12.109 or later.

Expand All @@ -160,15 +160,15 @@ 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.

```bash
$ 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, using parameter <tt>--platform ruby</tt>

```bash
$ sudo gem install statsample-optimization --platform ruby
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