RAP: Initialization of a RAP object

In rRAP: Real-Time Adaptive Penalization for Streaming Lasso Models

Description

This function initializes and RAP object. This contains a Lasso regression model together with methods to iteratively update the regularization parameter.

Usage

RAP(X, y, r = 0.95, eps = 0.01, l0 = 0.1, Approx = FALSE)

Arguments

X	Burn in training data. Can either be a single observation (in this case a matrix with 1 row) or several. This must be a matrix.
y	Burn in response data
r	Fixed forgetting factor used to update
eps	Fixed stepsize used to update regularization parameter
l0	Initial guess for regularization parameter
Approx	Boolean indicating whether exact or approximate gradient should be calculated when updating regularization parameter.

Details

See Monti et al, "A framework for adaptive regularization in streaming Lasso models", 2016

Value

A RAP object is returned with the following elements:

r	Fixed forgetting factor
eps	Stepsize used to update regularization parameter
w	Current measure of effective sample size
xbar
St
regParam	Current estimate of regularization parameter
l1Track	Vector storing all past estimates of regularization parameter
beta	Current estimate of regression coefficients
Approx	Boolean indicating if exact or approximate gradients where employed

The object has the following methods:

update	Update regularization parameters and regression coefficients based on new data
predict	Predict based on current model

Note

Warning that this implementation uses the shooting algorithm (co-ordinate gradient descent) to update regression coefficients. A more efficient implementation would employ stochastic gradient descent.

Author(s)

Ricardo Pio Monti

References

Monti et al, "A framework for adaptive regularization in streaming Lasso models", 2016

See Also

update.RAP, update.RAP

Examples

  # Recreate Figure 1 from 
  library(lars)
  data(diabetes)
  Data = cbind(diabetes$y, diabetes$x)
  # initialize RAP object
  R = RAP(X = matrix(diabetes$x[1,], nrow=1), y = diabetes$y[1], r = .995, eps = 0.0005, l0 = .1)
  # iteratively update:
  ## Not run: 
  for (i in 2:nrow(Data)){
    R = update.RAP(RAPobj=R, Ynew = diabetes$y[i], Xnew=matrix(diabetes$x[i,], nrow=1))
  }
  
## End(Not run)

rRAP documentation built on May 1, 2019, 9:30 p.m.