Module: Statsample::Regression
- Defined in:
- lib/statsample/regression.rb,
lib/statsample/regression/simple.rb,
lib/statsample/regression/multiple.rb,
lib/statsample/regression/multiple/gslengine.rb,
lib/statsample/regression/multiple/baseengine.rb,
lib/statsample/regression/multiple/rubyengine.rb,
lib/statsample/regression/multiple/alglibengine.rb,
lib/statsample/regression/multiple/matrixengine.rb
Overview
Module for regression procedures.
Use the method on this class to generate analysis. If you need more control, you can create and control directly the objects who computes the regressions.
-
Simple Regression : Statsample::Regression::Simple
-
Multiple Regression: Statsample::Regression::Multiple
-
Logit Regression: Statsample::Regression::Binomial::Logit
-
Probit Regression: Statsample::Regression::Binomial::Probit
Defined Under Namespace
Modules: Multiple Classes: Simple
Constant Summary collapse
- LinearDependency =
Class.new(Exception)
Class Method Summary collapse
-
.multiple(ds, y_var, opts = Hash.new) ⇒ Object
Creates one of the Statsample::Regression::Multiple object, for OLS multiple regression.
-
.simple(x, y) ⇒ Object
Create a Statsample::Regression::Simple object, for simple regression * x: independent Vector * y: dependent Vector Usage: x = Daru::Vector.new(100.times.collect {|i| rand(100)}) y = Daru::Vector.new(100.times.collect {|i| 2+x*2+rand()}) sr=Statsample::Regression.simple(x,y) sr.a => 2.51763295177808 sr.b => 1.99973746599856 sr.r => 0.999987881153254.
Class Method Details
.multiple(ds, y_var, opts = Hash.new) ⇒ Object
Creates one of the Statsample::Regression::Multiple object, for OLS multiple regression. Parameters:
-
ds
: Dataset. -
y: Name of dependent variable.
-
opts: A hash with options
-
missing_data: Could be
-
:listwise: delete cases with one or more empty data (default).
-
:pairwise: uses correlation matrix. Use with caution.
-
-
Usage:
lr=Statsample::Regression::multiple(ds,:y)
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# File 'lib/statsample/regression.rb', line 53 def self.multiple(ds,y_var, opts=Hash.new) missing_data= (opts[:missing_data].nil? ) ? :listwise : opts.delete(:missing_data) if missing_data==:pairwise Statsample::Regression::Multiple::RubyEngine.new(ds,y_var, opts) else if Statsample.has_gsl? and false Statsample::Regression::Multiple::GslEngine.new(ds, y_var, opts) else ds2=ds.dup_only_valid Statsample::Regression::Multiple::RubyEngine.new(ds2,y_var, opts) end end end |
.simple(x, y) ⇒ Object
Create a Statsample::Regression::Simple object, for simple regression
-
x: independent Vector
-
y: dependent Vector
Usage:
x = Daru::Vector.new(100.times.collect {|i| rand(100)})
y = Daru::Vector.new(100.times.collect {|i| 2+x[i]*2+rand()})
sr=Statsample::Regression.simple(x,y)
sr.a
=> 2.51763295177808
sr.b
=> 1.99973746599856
sr.r
=> 0.999987881153254
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# File 'lib/statsample/regression.rb', line 37 def self.simple(x,y) Statsample::Regression::Simple.new_from_vectors(x,y) end |