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

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

Overview

Class for Multiple Regression Analysis Requires rbgsl and uses a listwise aproach. Slower on prediction of values than Alglib, because predict is ruby based. Better memory management on multiple (+1000) series of regression. If you need pairwise, use RubyEngine Example:

@a = Daru::Vector.new([1,3,2,4,3,5,4,6,5,7])
@b = Daru::Vector.new([3,3,4,4,5,5,6,6,4,4])
@c = Daru::Vector.new([11,22,30,40,50,65,78,79,99,100])
@y = Daru::Vector.new([3,4,5,6,7,8,9,10,20,30])
ds = Daru::DataFrame.new({:a => @a,:b => @b,:c => @c,:y => @y})
lr=Statsample::Regression::Multiple::GslEngine.new(ds,:y)

Instance Attribute Summary

Attributes inherited from BaseEngine

#cases, #digits, #name, #total_cases, #valid_cases

Class Method Summary collapse

Instance Method Summary collapse

Methods inherited from BaseEngine

#anova, #assign_names, #coeffs_t, #coeffs_tolerances, #constant_se, #constant_t, #df_e, #df_r, #estimated_variance_covariance_matrix, #f, #mse, #msr, #predicted, #probability, #process, #r2_adjusted, #report_building, #residuals, #se_estimate, #se_r2, #sse, #sse_direct, #ssr, #ssr_direct, #standarized_predicted, #tolerance, univariate?

Methods included from Summarizable

#summary

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.reject_values(*Daru::MISSING_VALUES)
  @ds_valid    = @ds
  @valid_cases = @ds_valid.nrows
  @dy          = @ds[@y_var]
  @ds_indep    = ds.dup(ds.vectors.to_a - [y_var])
  # Create a custom matrix
  columns=[]
  @fields=[]
  max_deps = GSL::Matrix.alloc(@ds.nrows, @ds.vectors.size)
  constant_col=@ds.vectors.size-1
  for i in 0...@ds.nrows
    max_deps.set(i,constant_col,1)
  end
  j = 0
  @ds.vectors.each do |f|
    if f != @y_var
      @ds[f].each_index do |i1|
        max_deps.set(i1,j,@ds[f][i1])
      end

      columns.push(@ds[f].to_a)
      @fields.push(f)
      j += 1
    end
  end
  @dep_columns = columns.dup
  @lr_s        = nil
  c, @cov, @chisq, @status = GSL::MultiFit.linear(max_deps, @dy.to_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 59

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 56

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 100

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

#coeffs ⇒ Object 



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 64

def coeffs
  @coeffs
end

#coeffs_se ⇒ Object

Standard error for coeffs



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 115

def coeffs_se
  out  = {}
  evcm = estimated_variance_covariance_matrix
  @ds_valid.vectors.to_a.each_with_index do |f,i|
    mi = i+1
    next if f == @y_var
    out[f] = evcm[mi,mi]
  end
  out
end

#constant ⇒ Object 



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 87

def constant
  @constant
end

#lr_s ⇒ Object 



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 94

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 69

def matrix_resolution
  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 104

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

#r ⇒ Object 



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 81

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

#r2 ⇒ Object 



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 78

def r2
  r**2
end

#sst ⇒ Object 



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 84

def sst
  @dy.ss
end

#standarized_coeffs ⇒ Object 



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# File 'lib/statsample/regression/multiple/gslengine.rb', line 90

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 108

def standarized_residuals
  res=residuals
  red_sd=residuals.sds
  Daru::Vector.new(res.collect {|v| v.quo(red_sd) })
end