Class: Statsample::Regression::GLM::Logistic

Inherits:
Object
  • Object
show all
Defined in:
lib/bio-statsample-glm/regression/logistic.rb

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(ds, y) ⇒ Logistic

Returns a new instance of Logistic.



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# File 'lib/bio-statsample-glm/regression/logistic.rb', line 19

def initialize(ds, y)
  @ds=ds
  @fields=@ds.fields
  @x = ds.to_matrix
  @y = y
end

Instance Attribute Details

#converged ⇒ Object (readonly)

Boolean. Tells whether the IRWLS for the given model converged or not



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# File 'lib/bio-statsample-glm/regression/logistic.rb', line 17

def converged
  @converged
end

#df ⇒ Object (readonly)

The residuals degree of freedom



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# File 'lib/bio-statsample-glm/regression/logistic.rb', line 13

def df
  @df
end

#fit ⇒ Object (readonly)

The fitted mean values



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# File 'lib/bio-statsample-glm/regression/logistic.rb', line 9

def fit
  @fit
end

#iter ⇒ Object (readonly)

Number of iterations used for convergence



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# File 'lib/bio-statsample-glm/regression/logistic.rb', line 15

def iter
  @iter
end

#residuals ⇒ Object (readonly)

the working residuals; that is the residuals in the final iteration of the IRWLS fit.



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# File 'lib/bio-statsample-glm/regression/logistic.rb', line 11

def residuals
  @residuals
end

#se ⇒ Object (readonly)

Returns the value of attribute se.



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# File 'lib/bio-statsample-glm/regression/logistic.rb', line 7

def se
  @se
end

Class Method Details

.h(x, b, y) ⇒ Object 



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# File 'lib/bio-statsample-glm/regression/logistic.rb', line 62

def self.h(x,b,y)
  x_t = x.transpose
  mu_flat = mu(x,b).column_vectors.map(&:to_a).flatten
  column_data = y.zip(mu_flat).collect { |x| x.inject(:-) }
  x_t * Matrix.column_vector(column_data)
end

.j(x, b) ⇒ Object 



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# File 'lib/bio-statsample-glm/regression/logistic.rb', line 69

def self.j(x,b)
  w_matrix = w(x, b)
  jacobian_matrix = x.transpose * w_matrix * x
  jacobian_matrix.map { |x| -x }
end

.mu(x, b) ⇒ Object 



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# File 'lib/bio-statsample-glm/regression/logistic.rb', line 41

def self.mu(x, b)
  matrix_mul = x * b
  numerator = matrix_mul.map { |y| Math.exp(y) }
  denominator = numerator.map { |y| 1 + y }

  numerator.each_with_index { |e, r, c|
    numerator[r,c] = numerator[r,c].to_f / denominator[r,c].to_f
  }
end

.w(x, b) ⇒ Object 



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# File 'lib/bio-statsample-glm/regression/logistic.rb', line 51

def self.w(x, b)
  mus = mu(x,b).column_vectors.map(&:to_a).flatten
  mus_intermediate = mus.collect { |x| 1 - x }
  w = mus.zip(mus_intermediate).collect { |x| x.inject(:*) }
  w_mat = Matrix.I(w.size)
  w_enum = w.to_enum
  return w_mat.map do |x|
    x.eql?(1) ? w_enum.next : x
  end
end

Instance Method Details

#coefficients(type = :array) ⇒ Object

named vector/hash of coefficients

Parameter

  • type: symbol; (:array, default). Options = [:array, :hash]



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# File 'lib/bio-statsample-glm/regression/logistic.rb', line 29

def coefficients(type=:array)
  if type==:array
    #originally returned as vector; so pass it
    @coefficients
  elsif type==:hash
    h={}
    @fields.size.times {|i|
      h[@fields[i]]=@coefficients[i]
    }
    h
  end
end

#irwls ⇒ Object

Iteratively reweighted least squares

Computes irwls for given model and parameters.

Usage

require 'bio-statsample-glm'
x1=Statsample::Vector.new([0.537322309644812,-0.717124209978434,-0.519166718891331,0.434970973986765,-0.761822002215759,1.51170030921189,0.883854199811195,-0.908689798854196,1.70331977539793,-0.246971150634099,-1.59077593922623,-0.721548040910253,0.467025703920194,-0.510132788447137,0.430106510266798,-0.144353683251536,-1.54943800728303,0.849307651309298,-0.640304240933579,1.31462478279425,-0.399783455165345,0.0453055645017902,-2.58212161987746,-1.16484414309359,-1.08829266466281,-0.243893919684792,-1.96655661929441,0.301335373291024,-0.665832694463588,-0.0120650855753837,1.5116066367604,0.557300353673344,1.12829931872045,0.234443748015922,-2.03486690662651,0.275544751380246,-0.231465849558696,-0.356880153225012,-0.57746647541923,1.35758352580655,1.23971669378224,-0.662466275100489,0.313263561921793,-1.08783223256362,1.41964722846899,1.29325100940785,0.72153880625103,0.440580131022748,0.0351917814720056, -0.142353224879252],:scale)
x2=Statsample::Vector.new([-0.866655707911859,-0.367820249977585,0.361486610435,0.857332626245179,0.133438466268095,0.716104533073575,1.77206093023382,-0.10136697295802,-0.777086491435508,-0.204573554913706,0.963353531412233,-1.10103024900542,-0.404372761837392,-0.230226345183469,0.0363730246866971,-0.838265540390497,1.12543549657924,-0.57929175648001,-0.747060244805248,0.58946979365152,-0.531952663697324,1.53338594419818,0.521992029051441,1.41631763288724,0.611402316795129,-0.518355638373296,-0.515192557101107,-0.672697937866108,1.84347042325327,-0.21195540664804,-0.269869371631611,0.296155694010096,-2.18097898069634,-1.21314663927206,1.49193669881581,1.38969280369493,-0.400680808117106,-1.87282814976479,1.82394870451051,0.637864732838274,-0.141155946382493,0.0699950644281617,1.32568550595165,-0.412599258349398,0.14436832227506,-1.16507785388489,-2.16782049922428,0.24318371493798,0.258954871320764,-0.151966534521183],:scale)
y=Statsample::Vector.new([0,0,1,0,1,1,1,1,0,1,1,1,1,0,1,0,1,1,0,1,0,1,1,1,1,0,0,1,1,0,0,1,0,0,1,1,0,0,1,1,0,1,1,1,1,0,0,0,1,1],:scale)
x=Statsample::Dataset.new({"i"=>intercept,"x1"=>x1,"x2"=>x2})
obj = Statsample::Regression.glm(x, y, :binomial)
#=> Logistic Regression object
obj.irlws
#=> Array of returned values
obj.coefficients
#=> named vector of coefficients



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# File 'lib/bio-statsample-glm/regression/logistic.rb', line 95

def irwls
  x, y = @x, @y
  #calling irwls on Regression and passing equivalent methods in lambdas.
  #Ruby_level+=awesome!
  @coefficients, @se, @fit, @residuals, @df, @iter, @converged = Statsample::Regression.irwls(
      x,y, ->l,m{self.class.mu(l,m)}, ->l,m{self.class.w(l,m)},
      ->l,m{self.class.j(l,m)}, ->k,l,m{self.class.h(k,l,m)}
  )
end

#to_s ⇒ Object 



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# File 'lib/bio-statsample-glm/regression/logistic.rb', line 75

def to_s
  sprintf("Logistic Regression (Statsample::Regression::GLM;:Logistic)")
end