rename pkg --> aggexp

[aggexp.git] / pkg / R / m_RidgeRegression.R

diff --git a/pkg/R/m_RidgeRegression.R b/pkg/R/m_RidgeRegression.R
deleted file mode 100644 (file)
index 020894d..0000000
--- a/pkg/R/m_RidgeRegression.R
+++ /dev/null
@@ -1,49 +0,0 @@
-#' @include b_LinearAlgorithm.R
-
-#' @title Ridge Regression Algorithm
-#'
-#' @description Ridge Regression Algorithm.
-#' Inherits \code{\link{LinearAlgorithm}}
-#'
-#' @field lambda Value of lambda (let undefined for cross-validation). Default: undefined
-#' @field lambdas Vector of "optimal" lambda values over time. TODO: remove for production
-#'
-RidgeRegression = setRefClass(
-       Class = "RidgeRegression",
-
-       fields = c(
-               lambda = "numeric",
-               lambdas = "numeric"
-       ),
-
-       contains = "LinearAlgorithm",
-       
-       methods = list(
-               predict_noNA = function(XY, x)
-               {
-                       if (length(lambda) > 0 || nrow(XY) < 30) #TODO: magic number
-                       {
-                               #simple ridge regression with fixed lambda (not enough history for CV)
-                               X = matricize(XY[,names(XY) != "Measure"])
-                               Y = XY[,"Measure"]
-                               lambda_ = ifelse(length(lambda) > 0, lambda, LAMBDA)
-                               weight = ridgeSolve(X, Y, lambda_)
-                       }
-
-                       else
-                       {
-                               #enough data for cross-validations
-                               require(MASS, quietly=TRUE)
-                               gridLambda = seq(0.05,5.05,0.1)
-                               res_lmr = lm.ridge(Measure ~ . + 0, data=XY, lambda = gridLambda)
-                               lambda_ = res_lmr$lambda[which.min(res_lmr$GCV)]
-                               weight = as.matrix(coef(res_lmr))[which.min(res_lmr$GCV),]
-                       }
-
-                       lambdas <<- c(lambdas, lambda_)
-
-                       appendWeight(weight)
-                       return (matricize(x) %*% weight)
-               }
-       )
-)