Methods_LASSO: LASSO methods

Description Usage Arguments Value Author(s) Examples

Description

Predicted values for a provided matrix of predictors X

Usage

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## S3 method for class 'LASSO'
fitted(object, ...)

Arguments

object

An object of the class 'LASSO' returned either by the function 'lars2' or 'solveEN'

...

Other arguments: X (numeric matrix) scores for as many predictors there are in ncol(object$beta) (in columns) for a desired number n of observations (in rows)

Value

Returns a matrix that contains, for each value of lambda (in columns), the predicted values corresponding to each row of the matrix X

Author(s)

Marco Lopez-Cruz (maraloc@gmail.com) and Gustavo de los Campos

Examples

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  require(SFSI)
  data(wheatHTP)
  
  y = as.vector(Y[,"E1"])  # Response variable
  X = scale(X_E1)          # Predictors
  
  # Training and testing sets
  tst = seq(1,length(y),by=3)
  trn = seq_along(y)[-tst]

  # Calculate covariances in training set
  XtX = var(X[trn,])
  Xty = cov(y[trn],X[trn,])
  
  # Run the penalized regression
  fm = solveEN(XtX,Xty,alpha=0.5)   
  
  # Predicted values
  yHat1 = fitted(fm, X=X[trn,])  # training data
  yHat2 = fitted(fm, X=X[tst,])  # testing data
  
  # Penalization vs correlation
  plot(-log(fm$lambda[-1]),cor(y[trn],yHat1[,-1]), main="training")
  plot(-log(fm$lambda[-1]),cor(y[tst],yHat2[,-1]), main="testing")

SFSI documentation built on Oct. 1, 2021, 1:08 a.m.