Module: Statsample::Shorthand
- Included in:
- Analysis::Suite
- Defined in:
- lib/statsample/shorthand.rb
Overview
Module which provide shorthands for many methods.
Class Method Summary collapse
-
.rnorm(n, mean = 0, sd = 1) ⇒ Object
Random generation for the normal distribution.
- .test_u(*args) ⇒ Object
Instance Method Summary collapse
-
#boxplot(*args) ⇒ Object
Returns a Statsample::Graph::Boxplot.
-
#cor(ds) ⇒ Object
Create a correlation matrix from a dataset.
-
#cov(ds) ⇒ Object
Create a variance/covariance matrix from a dataset.
-
#dataset(vectors = Hash.new) ⇒ Object
(also: #data_frame)
Creates a new Daru::DataFrame Each key is transformed into a Symbol wherever possible.
- #dominance_analysis(*args) ⇒ Object
- #dominance_analysis_bootstrap(*args) ⇒ Object
-
#histogram(*args) ⇒ Object
Returns a Statsample::Graph::Histogram.
-
#levene(*args) ⇒ Object
Returns a Statsample::Test::Levene.
-
#lr(*args) ⇒ Object
Other Shortcuts.
- #multiscale_analysis(*args, &block) ⇒ Object
-
#names(ds) ⇒ Object
Retrieve names (fields) from dataset.
- #pca(ds, opts = Hash.new) ⇒ Object
- #polychoric(*args) ⇒ Object
- #principal_axis(*args) ⇒ Object
-
#read_csv(filename, opts = Hash.new) ⇒ Object
Import an CSV file.
-
#read_excel(filename, opts = Hash.new) ⇒ Object
Import an Excel file.
- #scale_analysis(*args) ⇒ Object
-
#scatterplot(*args) ⇒ Object
Returns a Statsample::Graph::Scatterplot.
- #skill_scale_analysis(*args) ⇒ Object
- #tetrachoric(*args) ⇒ Object
-
#vector(*args) ⇒ Object
Create a Daru::Vector Analog to R’s c.
Class Method Details
.rnorm(n, mean = 0, sd = 1) ⇒ Object
Random generation for the normal distribution
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# File 'lib/statsample/shorthand.rb', line 43 def rnorm(n,mean=0,sd=1) rng=Distribution::Normal.rng(mean,sd) Daru::Vector.new_with_size(n) { rng.call} end |
.test_u(*args) ⇒ Object
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# File 'lib/statsample/shorthand.rb', line 119 def test_u(*args) Statsample::Test::UMannWhitney.new(*args) end |
Instance Method Details
#boxplot(*args) ⇒ Object
Returns a Statsample::Graph::Boxplot
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# File 'lib/statsample/shorthand.rb', line 59 def boxplot(*args) Statsample::Graph::Boxplot.new(*args) end |
#cor(ds) ⇒ Object
Create a correlation matrix from a dataset
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# File 'lib/statsample/shorthand.rb', line 30 def cor(ds) Statsample::Bivariate.correlation_matrix(ds) end |
#cov(ds) ⇒ Object
Create a variance/covariance matrix from a dataset
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# File 'lib/statsample/shorthand.rb', line 34 def cov(ds) Statsample::Bivariate.covariate_matrix(ds) end |
#dataset(vectors = Hash.new) ⇒ Object Also known as: data_frame
Creates a new Daru::DataFrame Each key is transformed into a Symbol wherever possible.
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# File 'lib/statsample/shorthand.rb', line 49 def dataset(vectors=Hash.new) vectors = vectors.inject({}) do |ac,v| n = v[0].respond_to?(:to_sym) ? v[0].to_sym : v[0] ac[n] = v[1] ac end Daru::DataFrame.new(vectors) end |
#dominance_analysis(*args) ⇒ Object
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# File 'lib/statsample/shorthand.rb', line 99 def dominance_analysis(*args) Statsample::DominanceAnalysis.new(*args) end |
#dominance_analysis_bootstrap(*args) ⇒ Object
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# File 'lib/statsample/shorthand.rb', line 103 def dominance_analysis_bootstrap(*args) Statsample::DominanceAnalysis::Bootstrap.new(*args) end |
#histogram(*args) ⇒ Object
Returns a Statsample::Graph::Histogram
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# File 'lib/statsample/shorthand.rb', line 63 def histogram(*args) Statsample::Graph::Histogram.new(*args) end |
#levene(*args) ⇒ Object
Returns a Statsample::Test::Levene
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# File 'lib/statsample/shorthand.rb', line 72 def levene(*args) Statsample::Test::Levene.new(*args) end |
#lr(*args) ⇒ Object
Other Shortcuts
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# File 'lib/statsample/shorthand.rb', line 91 def lr(*args) Statsample::Regression.multiple(*args) end |
#multiscale_analysis(*args, &block) ⇒ Object
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# File 'lib/statsample/shorthand.rb', line 115 def multiscale_analysis(*args,&block) Statsample::Reliability::MultiScaleAnalysis.new(*args,&block) end |
#names(ds) ⇒ Object
Retrieve names (fields) from dataset
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# File 'lib/statsample/shorthand.rb', line 26 def names(ds) ds.vectors.to_a end |
#pca(ds, opts = Hash.new) ⇒ Object
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# File 'lib/statsample/shorthand.rb', line 95 def pca(ds,opts=Hash.new) Statsample::Factor::PCA.new(ds,opts) end |
#polychoric(*args) ⇒ Object
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# File 'lib/statsample/shorthand.rb', line 80 def polychoric(*args) Statsample::Bivariate::Polychoric.new(*args) end |
#principal_axis(*args) ⇒ Object
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# File 'lib/statsample/shorthand.rb', line 76 def principal_axis(*args) Statsample::Factor::PrincipalAxis.new(*args) end |
#read_csv(filename, opts = Hash.new) ⇒ Object
Import an CSV file. Cache result by default
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# File 'lib/statsample/shorthand.rb', line 21 def read_csv(filename, opts=Hash.new) Daru::DataFrame.from_csv filename, opts end |
#read_excel(filename, opts = Hash.new) ⇒ Object
Import an Excel file. Cache result by default
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# File 'lib/statsample/shorthand.rb', line 16 def read_excel(filename, opts=Hash.new) Daru::DataFrame.from_excel filename, opts end |
#scale_analysis(*args) ⇒ Object
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# File 'lib/statsample/shorthand.rb', line 107 def scale_analysis(*args) Statsample::Reliability::ScaleAnalysis.new(*args) end |
#scatterplot(*args) ⇒ Object
Returns a Statsample::Graph::Scatterplot
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# File 'lib/statsample/shorthand.rb', line 68 def scatterplot(*args) Statsample::Graph::Scatterplot.new(*args) end |
#skill_scale_analysis(*args) ⇒ Object
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# File 'lib/statsample/shorthand.rb', line 111 def skill_scale_analysis(*args) Statsample::Reliability::SkillScaleAnalysis.new(*args) end |
#tetrachoric(*args) ⇒ Object
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# File 'lib/statsample/shorthand.rb', line 84 def tetrachoric(*args) Statsample::Bivariate::Tetrachoric.new(*args) end |