/KRLS.jl

Implements the KRLS method in Julia

Primary LanguageJuliaOtherNOASSERTION

KRLS in Julia

This script (very much under construction) implements Kernel Regularized Least Squares (paper here) in the Julia language. While much more is on the way, it is currently mostly a transliteration from the R package (found here). It is also currently about 10 times faster!

Functions

krls

Arguments

Required

  • Xinit - your data matrix, observations are the first dimension, features are the second dimension
  • yinit - your response vector

Optional

  • lambda - default is to fit using leave-one-out-cross-validation, but the user can specify any number greater than 0

Returns

The method only outputs one object, a KRLS object, from which the following can be retrieved:

  • K - the kernel matrix
  • coeffs - the choice coefficients
  • Looe - the final leave-one-out error
  • fitted - fitted y values
  • X - original X matrix
  • y - original y vector
  • sigma
  • lambda
  • R2 - R-squared
  • derivatives - the pointwise marginal effects
  • avgderivatives - the average pointwise marginal effects
  • var_avgderivatives - variance of the average pointwise marginal effects
  • vcov_c - variance covariance matrix of the choice coefficients
  • vcov_fitted - variance covariance matrix of the fitted values

predict

This can be used to predict outcomes for new data given a new data matrix with the same number of columns.

Arguments

  • k - a KRLS object
  • newmatint a new data matrix with the same number of columns

Returns

Returns a 3-tuple with the following objects (this will be changed soon):

  • yfitted - predicted y values
  • sefit - standard errors of the predicted values
  • vcovfit - variance covariance matrix of the predicted values

Example

X = randn(1000, 3)
X = hcat(X, vcat(repmat([1], 500, 1), repmat([0], 500, 1)))
y = X * [1,2,3, -2] + randn(1000)
k = krls(X, y)
KRLS results
------------------------------------
Average Marginal Effects:
1x4 Array{Float64,2}:
 1.0059  1.9553  2.8991  -1.9689
Quartiles of Marginal Effects
Var 1: [0.828,1.0257,1.1908]
Var 2: [1.7346,2.0057,2.2355]
Var 3: [2.6737,2.91,3.1563]
Var 4: [-2.207,-2.0203,-1.7358]

k.coeffs
1000-element Array{Float64,1}:
 -2.10438   
  2.16206   
  1.32769   
  2.9391    
 -2.04586   
 -3.5121    
 -2.07879   
  3.32383   
  2.01171   
  1.10001   
 -2.06835   
 -4.77452   
  0.00034328
  ⋮         
 -2.5586    
 -1.11627   
 -1.32929   
  1.04863   
 -1.33211   
  2.51199   
  1.45449   
  2.41898   
 -1.9107    
  1.75867   
 -2.39654   
 -0.205217  

hcat(y, k.fitted)
1000x2 Array{Float64,2}:
 -6.28847   -5.4086  
 -2.36659   -3.27057
  0.206222  -0.348901
 -1.76807   -2.99694
 -0.361033   0.494366
 -4.45088   -2.98243
 -1.57046   -0.701293
  1.97831    0.588583
  3.69702    2.8559  
 -6.09572   -6.55565
 -3.53623   -2.67143
  0.304973   2.30125
 -4.1711    -4.17125
  ⋮                  
  5.04699    6.11677
  5.15348    5.62021
 -5.28853   -4.73274
  6.66882    6.23037
  1.31033    1.8673  
 -2.07486   -3.12515
  0.478735  -0.129404
  3.381      2.3696  
 -0.208332   0.590553
  6.1462     5.41089
 -3.31874   -2.31672
 -4.0134    -3.9276

Performance

Later I will include more formal speed tests comparing this with KRLS in R and Stata. For now, this (mostly) transliteration of the R package results in speed improvements of 10-20 times.

To do

  • Input validation, more user feedback
  • Column names, possibly use DataFrames
  • Methods for interpretation
  • Cleaning up code
  • Speed improvements