# SciRuby/statsample

lib/statsample/regression/multiple/rubyengine.rb

### Summary

A
1 hr
###### Test Coverage
``````module Statsample
module Regression
module Multiple
# Pure Ruby Class for Multiple Regression Analysis.
# Slower than AlglibEngine, but is pure ruby and can use a pairwise aproach for missing values.
# Coeffient calculation uses correlation matrix between the vectors
# If you need listwise aproach for missing values, use AlglibEngine, because is faster.
#
# 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::RubyEngine.new(ds,:y)

class RubyEngine < MatrixEngine
def initialize(ds,y_var, opts=Hash.new)
matrix = Statsample::Bivariate.correlation_matrix ds
fields_indep=ds.vectors.to_a - [y_var]
default= {
:y_mean => ds[y_var].mean,
:x_mean => fields_indep.inject({}) {|ac,f|  ac[f]=ds[f].mean; ac},
:y_sd   => ds[y_var].sd,
:x_sd   => fields_indep.inject({}) {|ac,f|  ac[f]=ds[f].sd; ac},
:cases  => Statsample::Bivariate.min_n_valid(ds)
}
opts = opts.merge(default)
super(matrix, y_var, opts)
@ds = ds
@dy = ds[@y_var]
@ds_valid = ds.reject_values(*Daru::MISSING_VALUES)
@total_cases = @ds.nrows
@valid_cases = @ds_valid.nrows
@ds_indep    = ds.dup(ds.vectors.to_a - [y_var])
set_dep_columns
end

def set_dep_columns
@dep_columns = []
@ds_indep.each_vector { |v| @dep_columns.push(v.to_a) }
end

def fix_with_mean
i=0
@ds_indep.each(:row) do |row|
empty=[]
row.each do |k,v|
empty.push(k) if v.nil?
end

if empty.size==1
@ds_indep[empty[0]][i]=@ds[empty[0]].mean
end
i += 1
end
set_dep_columns
end
def fix_with_regression
i = 0
@ds_indep.each(:row) do |row|
empty = []
row.each { |k,v| empty.push(k) if v.nil? }
if empty.size==1
field  = empty[0]
lr     = MultipleRegression.new(@ds_indep,field)
fields = []
@ds_indep.vectors.each { |f|
fields.push(row[f]) unless f == field
}

@ds_indep[field][i]=lr.process(fields)
end
i+=1
end
set_dep_columns
end
# Standard error for constant
def constant_se
estimated_variance_covariance_matrix[0,0]
end
end
end
end
end``````