Class: Rubythinking::Quap

Inherits:
Object
  • Object
show all
Defined in:
lib/rubythinking/quap.rb

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(formulas:, data:, start: nil) ⇒ Quap

Returns a new instance of Quap.



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# File 'lib/rubythinking/quap.rb', line 15

def initialize(formulas:, data:, start: nil)
  @formulas = formulas
  @data = data
  @start = start
  @estimated = false
  @coef = nil
  @vcov = nil
  
  validate_formulas!
  validate_data!
end

Instance Attribute Details

#data ⇒ Object (readonly)

Returns the value of attribute data.



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# File 'lib/rubythinking/quap.rb', line 13

def data
  @data
end

#formulas ⇒ Object (readonly)

Returns the value of attribute formulas.



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# File 'lib/rubythinking/quap.rb', line 13

def formulas
  @formulas
end

#start ⇒ Object (readonly)

Returns the value of attribute start.



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# File 'lib/rubythinking/quap.rb', line 13

def start
  @start
end

Instance Method Details

#aic ⇒ Object 



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# File 'lib/rubythinking/quap.rb', line 104

def aic
  check_estimated!
  -2 * loglik + 2 * npar
end

#coef ⇒ Object 



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# File 'lib/rubythinking/quap.rb', line 50

def coef
  check_estimated!
  @coef
end

#estimate ⇒ Object 



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# File 'lib/rubythinking/quap.rb', line 31

def estimate
  # Get initial parameter values
  param_names = parameters
  initial_values = get_initial_values(param_names)
  
  # Optimize log-likelihood using Nelder-Mead
  result = nelder_mead_optimize(initial_values, param_names)
  
  # Store results
  @coef = param_names.zip(result[:x]).to_h
  @log_likelihood = -result[:fval]  # Convert from negative log-likelihood
  
  # Calculate variance-covariance matrix using numerical Hessian
  @vcov = calculate_vcov(result[:x], param_names)
  
  @estimated = true
  self
end

#estimated? ⇒ Boolean

Returns:



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# File 'lib/rubythinking/quap.rb', line 27

def estimated?
  @estimated
end

#loglik ⇒ Object 



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# File 'lib/rubythinking/quap.rb', line 94

def loglik
  check_estimated!
  @log_likelihood
end

#npar ⇒ Object 



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# File 'lib/rubythinking/quap.rb', line 99

def npar
  check_estimated!
  @coef.length
end

#samples(n: 1000, seed: nil) ⇒ Object 



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# File 'lib/rubythinking/quap.rb', line 82

def samples(n: 1000, seed: nil)
  check_estimated!
  srand(seed) if seed
  
  result = {}
  @coef.each_with_index do |(param, value), i|
    variance = @vcov[i, i]
    result[param] = Distributions::Normal.samples(n, value, Math.sqrt(variance))
  end
  result
end

#se ⇒ Object 



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# File 'lib/rubythinking/quap.rb', line 60

def se
  check_estimated!
  diagonal = @vcov.each(:diagonal).to_a
  param_names = @coef.keys
  param_names.zip(diagonal.map { |v| Math.sqrt(v) }).to_h
end

#summary ⇒ Object 



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# File 'lib/rubythinking/quap.rb', line 67

def summary
  check_estimated!
  
  lines = []
  lines << "Quadratic approximation"
  lines << ""
  lines << "Parameter estimates:"
  @coef.each do |param, value|
    stderr = se[param]
    lines << "  #{param}: #{value} (SE: #{stderr})"
  end
  
  lines.join("\n")
end

#vcov ⇒ Object 



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# File 'lib/rubythinking/quap.rb', line 55

def vcov
  check_estimated!
  @vcov
end