Class: Statsample::DominanceAnalysis::Bootstrap

Inherits:
Object
  • Object
show all
Includes:
Summarizable, Writable
Defined in:
lib/statsample/dominanceanalysis/bootstrap.rb

Overview

Goal

Generates Bootstrap sample to identity the replicability of a Dominance Analysis. See Azen & Bodescu (2003) for more information.

Usage

require 'statsample' a = Daru::Vector.new(100.times.collect rand) b = Daru::Vector.new(100.times.collect rand) c = Daru::Vector.new(100.times.collect rand) d = Daru::Vector.new(100.times.collect rand) ds = Daru::DataFrame.new(=> a,:b => b,:c => c,:d => d) ds = ds.collect_rows { |row| row*5+row*2+row*2+row*2+10*rand() } dab=Statsample::DominanceAnalysis::Bootstrap.new(ds, :y, :debug=>true) dab.bootstrap(100,nil) puts dab.summary <strong>Output</strong>

 Sample size: 100
t: 1.98421693632958

Linear Regression Engine: Statsample::Regression::Multiple::MatrixEngine
Table: Bootstrap report
--------------------------------------------------------------------------------------------
| pairs                 | sD  | Dij    | SE(Dij) | Pij   | Pji   | Pno   | Reproducibility |
--------------------------------------------------------------------------------------------
| Complete dominance    |
--------------------------------------------------------------------------------------------
| a - b                 | 1.0 | 0.6150 | 0.454   | 0.550 | 0.320 | 0.130 | 0.550           |
| a - c                 | 1.0 | 0.9550 | 0.175   | 0.930 | 0.020 | 0.050 | 0.930           |
| a - d                 | 1.0 | 0.9750 | 0.131   | 0.960 | 0.010 | 0.030 | 0.960           |
| b - c                 | 1.0 | 0.8800 | 0.276   | 0.820 | 0.060 | 0.120 | 0.820           |
| b - d                 | 1.0 | 0.9250 | 0.193   | 0.860 | 0.010 | 0.130 | 0.860           |
| c - d                 | 0.5 | 0.5950 | 0.346   | 0.350 | 0.160 | 0.490 | 0.490           |
--------------------------------------------------------------------------------------------
| Conditional dominance |
--------------------------------------------------------------------------------------------
| a - b                 | 1.0 | 0.6300 | 0.458   | 0.580 | 0.320 | 0.100 | 0.580           |
| a - c                 | 1.0 | 0.9700 | 0.156   | 0.960 | 0.020 | 0.020 | 0.960           |
| a - d                 | 1.0 | 0.9800 | 0.121   | 0.970 | 0.010 | 0.020 | 0.970           |
| b - c                 | 1.0 | 0.8850 | 0.283   | 0.840 | 0.070 | 0.090 | 0.840           |
| b - d                 | 1.0 | 0.9500 | 0.181   | 0.920 | 0.020 | 0.060 | 0.920           |
| c - d                 | 0.5 | 0.5800 | 0.360   | 0.350 | 0.190 | 0.460 | 0.460           |
--------------------------------------------------------------------------------------------
| General Dominance     |
--------------------------------------------------------------------------------------------
| a - b                 | 1.0 | 0.6500 | 0.479   | 0.650 | 0.350 | 0.000 | 0.650           |
| a - c                 | 1.0 | 0.9800 | 0.141   | 0.980 | 0.020 | 0.000 | 0.980           |
| a - d                 | 1.0 | 0.9900 | 0.100   | 0.990 | 0.010 | 0.000 | 0.990           |
| b - c                 | 1.0 | 0.9000 | 0.302   | 0.900 | 0.100 | 0.000 | 0.900           |
| b - d                 | 1.0 | 0.9700 | 0.171   | 0.970 | 0.030 | 0.000 | 0.970           |
| c - d                 | 1.0 | 0.5600 | 0.499   | 0.560 | 0.440 | 0.000 | 0.560           |
--------------------------------------------------------------------------------------------

Table: General averages
---------------------------------------
| var | mean  | se    | p.5   | p.95  |
---------------------------------------
| a   | 0.133 | 0.049 | 0.062 | 0.218 |
| b   | 0.106 | 0.048 | 0.029 | 0.199 |
| c   | 0.035 | 0.032 | 0.002 | 0.106 |
| d   | 0.023 | 0.019 | 0.002 | 0.062 |
---------------------------------------

References:

  • Azen, R. & Budescu, D.V. (2003). The dominance analysis approach for comparing predictors in multiple regression. Psychological Methods, 8(2), 129-148.

Constant Summary collapse

ALPHA =

Default level of confidence for t calculation

0.95

Instance Attribute Summary collapse

Instance Method Summary collapse

Methods included from Summarizable

#summary

Methods included from Writable

#save

Constructor Details

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

Create a new Dominance Analysis Bootstrap Object

  • ds: A Daru::DataFrame object

  • y_var: Name of dependent variable

  • opts: Any other attribute of the class


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# File 'lib/statsample/dominanceanalysis/bootstrap.rb', line 97

def initialize(ds,y_var, opts=Hash.new)
  @ds    = ds
  @y_var = y_var.respond_to?(:to_sym) ? y_var.to_sym : y_var
  @n     = ds.nrows
  
  @n_samples=0
  @alpha=ALPHA
  @debug=false
  if y_var.is_a? Array
    @fields=ds.vectors.to_a - y_var
    @regression_class=Regression::Multiple::MultipleDependent
    
  else
    @fields=ds.vectors.to_a - [y_var]
    @regression_class=Regression::Multiple::MatrixEngine
  end
  @samples_ga=@fields.inject({}) { |a,v| a[v]=[]; a }

  @name=_("Bootstrap dominance Analysis:  %s over %s") % [ ds.vectors.to_a.join(",") , @y_var]
  opts.each{|k,v|
    self.send("#{k}=",v) if self.respond_to? k
  }
  create_samples_pairs            
end

Instance Attribute Details

#alphaObject

Alpha level of confidence. Default: ALPHA


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# File 'lib/statsample/dominanceanalysis/bootstrap.rb', line 87

def alpha
  @alpha
end

#debugObject

Debug?


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# File 'lib/statsample/dominanceanalysis/bootstrap.rb', line 89

def debug
  @debug
end

#dsObject

Dataset


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# File 'lib/statsample/dominanceanalysis/bootstrap.rb', line 83

def ds
  @ds
end

#fieldsObject (readonly)

Name of fields


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# File 'lib/statsample/dominanceanalysis/bootstrap.rb', line 79

def fields
  @fields
end

#nameObject

Name of analysis


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# File 'lib/statsample/dominanceanalysis/bootstrap.rb', line 85

def name
  @name
end

#regression_classObject Also known as: lr_class

Regression class used for analysis


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# File 'lib/statsample/dominanceanalysis/bootstrap.rb', line 81

def regression_class
  @regression_class
end

#samples_cdObject (readonly)

Conditional Dominance results


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# File 'lib/statsample/dominanceanalysis/bootstrap.rb', line 73

def samples_cd
  @samples_cd
end

#samples_gaObject (readonly)

General average results


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# File 'lib/statsample/dominanceanalysis/bootstrap.rb', line 77

def samples_ga
  @samples_ga
end

#samples_gdObject (readonly)

General Dominance results


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# File 'lib/statsample/dominanceanalysis/bootstrap.rb', line 75

def samples_gd
  @samples_gd
end

#samples_tdObject (readonly)

Total Dominance results


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# File 'lib/statsample/dominanceanalysis/bootstrap.rb', line 71

def samples_td
  @samples_td
end

Instance Method Details

#bootstrap(number_samples, n = nil) ⇒ Object

Creates n re-samples from original dataset and store result of each sample on @samples_td, @samples_cd, @samples_gd, @samples_ga

  • number_samples: Number of new samples to add

  • n: size of each new sample. If nil, equal to original sample size


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# File 'lib/statsample/dominanceanalysis/bootstrap.rb', line 134

def bootstrap(number_samples,n=nil)
  number_samples.times{ |t|
    @n_samples+=1
    puts _("Bootstrap %d of %d") % [t+1, number_samples] if @debug
    ds_boot=@ds.bootstrap(n)                 
    da_1=DominanceAnalysis.new(ds_boot, @y_var, :regression_class => @regression_class)

    da_1.total_dominance.each{|k,v|
      @samples_td[k].push(v)
    }
    da_1.conditional_dominance.each{|k,v|
      @samples_cd[k].push(v)
    }
    da_1.general_dominance.each{|k,v|
      @samples_gd[k].push(v)
    }
    da_1.general_averages.each{|k,v|
      @samples_ga[k].push(v)
    }
  }
end

#create_samples_pairsObject


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# File 'lib/statsample/dominanceanalysis/bootstrap.rb', line 155

def create_samples_pairs
  @samples_td={}
  @samples_cd={}
  @samples_gd={}
  @pairs=[]
  c=(0...@fields.size).to_a.combination(2)
  c.each do |data|
    p data
    convert=data.collect {|i| @fields[i] }
    @pairs.push(convert)
    [@samples_td, @samples_cd, @samples_gd].each{|s|
      s[convert]=[]
    }
  end
end

#daObject


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# File 'lib/statsample/dominanceanalysis/bootstrap.rb', line 123

def da
  if @da.nil?
    @da=DominanceAnalysis.new(@ds,@y_var, :regression_class => @regression_class)
  end
  @da
end

#f(v, n = 3) ⇒ Object


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# File 'lib/statsample/dominanceanalysis/bootstrap.rb', line 226

def f(v,n=3)
    prec="%0.#{n}f"
    sprintf(prec,v)
end

#report_building(builder) ⇒ Object

:nodoc:


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# File 'lib/statsample/dominanceanalysis/bootstrap.rb', line 173

def report_building(builder) # :nodoc:
  raise "You should bootstrap first" if @n_samples==0
  builder.section(:name=>@name) do |generator|
    generator.text _("Sample size: %d\n") % @n_samples
    generator.text "t: #{t}\n"
    generator.text _("Linear Regression Engine: %s") % @regression_class.name
    
    table=ReportBuilder::Table.new(:name=>"Bootstrap report", :header => [_("pairs"), "sD","Dij", _("SE(Dij)"), "Pij", "Pji", "Pno", _("Reproducibility")])
    table.row([_("Complete dominance"),"","","","","","",""])
    table.hr
    @pairs.each{|pair|
      std=Daru::Vector.new(@samples_td[pair])
      ttd=da.total_dominance_pairwise(pair[0],pair[1])
      table.row(summary_pairs(pair,std,ttd))
    }
    table.hr
    table.row([_("Conditional dominance"),"","","","","","",""])
    table.hr
    @pairs.each{|pair|
      std=Daru::Vector.new(@samples_cd[pair])
      ttd=da.conditional_dominance_pairwise(pair[0],pair[1])
      table.row(summary_pairs(pair,std,ttd))
    
    }
    table.hr
    table.row([_("General Dominance"),"","","","","","",""])
    table.hr
    @pairs.each{|pair|
      std=Daru::Vector.new(@samples_gd[pair])
      ttd=da.general_dominance_pairwise(pair[0],pair[1])
      table.row(summary_pairs(pair,std,ttd))
    }
    generator.parse_element(table)
    
    table=ReportBuilder::Table.new(:name=>_("General averages"), :header=>[_("var"), _("mean"), _("se"), _("p.5"), _("p.95")])
    
    @fields.each{|f|
      v=Daru::Vector.new(@samples_ga[f])
      row=[@ds[f].name, sprintf("%0.3f",v.mean), sprintf("%0.3f",v.sd), sprintf("%0.3f",v.percentil(5)),sprintf("%0.3f",v.percentil(95))]
      table.row(row)          
    }
    
    generator.parse_element(table)
  end
end

#summary_pairs(pair, std, ttd) ⇒ Object


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# File 'lib/statsample/dominanceanalysis/bootstrap.rb', line 218

def summary_pairs(pair,std,ttd)
    freqs=std.proportions
    [0, 0.5, 1].each{|n|
        freqs[n]=0 if freqs[n].nil?
    }
    name="%s - %s" % [@ds[pair[0]].name, @ds[pair[1]].name]
    [name,f(ttd,1),f(std.mean,4),f(std.sd),f(freqs[1]), f(freqs[0]), f(freqs[0.5]), f(freqs[ttd])]
end

#tObject


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# File 'lib/statsample/dominanceanalysis/bootstrap.rb', line 170

def t
  Distribution::T.p_value(1-((1-@alpha) / 2), @n_samples - 1)
end