Class: Statsample::Regression::Multiple::GslEngine

Inherits:
BaseEngine
  • Object
show all
Defined in:
lib/statsample/regression/multiple/gslengine.rb

Instance Attribute Summary

Attributes inherited from BaseEngine

#name

Class Method Summary collapse

Instance Method Summary collapse

Methods inherited from BaseEngine

#assign_names, #coeffs_se, #coeffs_t, #coeffs_tolerances, #constant_se, #constant_t, #df_e, #df_r, #estimated_variance_covariance_matrix, #f, #mse, #msr, #predicted, #process, #report_building, #residuals, #se_r2, #significance, #sse, #sse_direct, #ssr, #ssr_direct, #standarized_predicted, #summary, #tolerance, univariate?

Constructor Details

#initialize(ds, y_var, opts = Hash.new) ⇒ GslEngine

Returns a new instance of GslEngine.



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 20

def initialize(ds,y_var, opts=Hash.new)
  super
  @ds=ds.dup_only_valid
  @ds_valid=@ds
  @dy=@ds[@y_var]
  @ds_indep=ds.dup(ds.fields-[y_var])
  # Create a custom matrix
  columns=[]
  @fields=[]
  max_deps = GSL::Matrix.alloc(@ds.cases, @ds.fields.size)
  constant_col=@ds.fields.size-1
  for i in 0...@ds.cases
      max_deps.set(i,constant_col,1)
  end
  j=0
  @ds.fields.each{|f|
      if f!=@y_var
          @ds[f].each_index{|i1|
              max_deps.set(i1,j,@ds[f][i1])
          }
          columns.push(@ds[f].to_a)
          @fields.push(f)
          j+=1
      end
  }
  @dep_columns=columns.dup
  @lr_s=nil
  c, @cov, @chisq, @status = GSL::MultiFit.linear(max_deps, @dy.gsl)
  @constant=c[constant_col]
  @coeffs_a=c.to_a.slice(0...constant_col)
  @coeffs=assign_names(@coeffs_a)
  c=nil
end

Class Method Details

._load(data) ⇒ Object 



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 57

def self._load(data)
  h=Marshal.load(data)
  self.new(h['ds'], h['y_var'])
end

Instance Method Details

#_dump(i) ⇒ Object 



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 54

def _dump(i)
  Marshal.dump({'ds'=>@ds,'y_var'=>@y_var})
end

#build_standarized ⇒ Object 



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 99

def build_standarized
  @ds_s=@ds.standarize
  @lr_s=GslEngine.new(@ds_s,@y_var)
end

#coeffs ⇒ Object 



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 62

def coeffs
  @coeffs
end

#constant ⇒ Object 



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 86

def constant
  @constant
end

#lr_s ⇒ Object 



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 93

def lr_s
  if @lr_s.nil?
      build_standarized
  end
  @lr_s
end

#matrix_resolution ⇒ Object

Coefficients using a constant Based on www.xycoon.com/ols1.htm



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 67

def matrix_resolution
  mse_p=mse
  columns=@dep_columns.dup.map {|xi| xi.map{|i| i.to_f}}
  columns.unshift([1.0]*@ds.cases)
  y=Matrix.columns([@dy.data.map  {|i| i.to_f}])
  x=Matrix.columns(columns)
  xt=x.t
  matrix=((xt*x)).inverse*xt
  matrix*y
end

#process_s(v) ⇒ Object 



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 103

def process_s(v)
  lr_s.process(v)
end

#r ⇒ Object 



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 80

def r
  Bivariate::pearson(@dy, predicted)
end

#r2 ⇒ Object 



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 77

def r2
  r**2
end

#sst ⇒ Object 



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 83

def sst
  @dy.ss
end

#standarized_coeffs ⇒ Object 



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 89

def standarized_coeffs
  l=lr_s
  l.coeffs
end

#standarized_residuals ⇒ Object

???? Not equal to SPSS output



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 107

def standarized_residuals
  res=residuals
  red_sd=residuals.sds
  res.collect {|v|
      v.quo(red_sd)
  }.to_vector(:scale)
end