From 0e649c891804f1d0d5fddf6b98573cf2a4e14edb Mon Sep 17 00:00:00 2001 From: Egon Willighagen Date: Wed, 29 Jul 2015 14:53:31 +0200 Subject: [PATCH] Followed Jose's tips on inst/models, which is what caret uses. Removed the docs again to not expose these models to the user --- RRegrs/R/RRegrs_Functions.R | 4 +- .../{data => inst/models}/glmnetModel.RData | Bin .../models}/model.svmRadialReg.RData | Bin RRegrs/man/glmnetModel.Rd | 37 ----------------- RRegrs/man/model.svmRadialReg.Rd | 39 ------------------ 5 files changed, 2 insertions(+), 78 deletions(-) rename RRegrs/{data => inst/models}/glmnetModel.RData (100%) rename RRegrs/{data => inst/models}/model.svmRadialReg.RData (100%) delete mode 100644 RRegrs/man/glmnetModel.Rd delete mode 100644 RRegrs/man/model.svmRadialReg.Rd diff --git a/RRegrs/R/RRegrs_Functions.R b/RRegrs/R/RRegrs_Functions.R index 8c5b7e2..0c67b6c 100644 --- a/RRegrs/R/RRegrs_Functions.R +++ b/RRegrs/R/RRegrs_Functions.R @@ -1886,7 +1886,7 @@ Yrandom<- function(dss,trainFrac,best.reg,best.R2.ts,noYrand,ResBestF,rfe_SVM_pa # jseoane # use: # svmFuncsGradW: RAKOTOMAMONJY gradient w -data(model.svmRadialReg) +load(system.file("models", "model.svmRadialReg.RData", package = "RRegrs")) svmFuncsW = caretFuncs ## regular ranking using w svmFuncsW$fit=function(x,y,first,last,...,tuneGrid){ @@ -2571,7 +2571,7 @@ ENETreg <- function(my.datf.train,my.datf.test,sCV,iSplit=1,fDet=F,outFile="") { #library(caret) #library(glmnet) - data(glmnetModel) + load(system.file("models", "glmnetModel.RData", package = "RRegrs")) net.c = my.datf.train[,1] # dependent variable is the first column in Training set RegrMethod <- "glmnet" # type of regression diff --git a/RRegrs/data/glmnetModel.RData b/RRegrs/inst/models/glmnetModel.RData similarity index 100% rename from RRegrs/data/glmnetModel.RData rename to RRegrs/inst/models/glmnetModel.RData diff --git a/RRegrs/data/model.svmRadialReg.RData b/RRegrs/inst/models/model.svmRadialReg.RData similarity index 100% rename from RRegrs/data/model.svmRadialReg.RData rename to RRegrs/inst/models/model.svmRadialReg.RData diff --git a/RRegrs/man/glmnetModel.Rd b/RRegrs/man/glmnetModel.Rd deleted file mode 100644 index d7cf86e..0000000 --- a/RRegrs/man/glmnetModel.Rd +++ /dev/null @@ -1,37 +0,0 @@ -\name{glmnetModel} -\alias{glmnetModel} -\docType{glmnetModel} - -\title{ -Specifications needed for glmnet model -} - -\description{ -A list of parameters needed for glmnet model -} - -\usage{data("glmnetModel")} - -\format{A list including all parameters needed for glmnet function in R caret package and specification for parameter space search, specifically: -\describe{ -\item{label}{a character string 'glmnet' to specify the model} -\item{library}{a character string 'glmnet' to specify the library used} -\item{type}{a charcater vector c('Regression', 'Classification') for NNreg function to specify the problem studied} -\item{parameters}{a 2x3 data frame of characters specifying the parameters used, namely alpha and lambda, their class and label} -\item{grid}{a grid function to specify the grid of the parameter spece for alpha and lambda; arguments are x (number of alpha values), y (number of lambda values) and len (length of sequence for both x,y)} -\item{loop}{a loop function over grid parameter to specify unique entries for grid; a list is produced with items loop (maximum parameter values) and submodels (remaining unique lambda values)} -\item{fit}{a function to specify fitting model} -\item{predict}{a function to specify prediction model} -\tem{prob}{prediction probabilities} -\item{predictors}{a function to generate predictions} -\item{varImp}{ a funtion calculate importance of variables} -\item{levels}{} -\item{tags}{a character vector including all possible models} -\item{sort}{a sort function used for parameters alpha, lambda} -} -} - -\examples{ -data(glmnetModel) -} -\author{Jose A. Seoane, Carlos Fernandez-Lozano} diff --git a/RRegrs/man/model.svmRadialReg.Rd b/RRegrs/man/model.svmRadialReg.Rd deleted file mode 100644 index bdbc292..0000000 --- a/RRegrs/man/model.svmRadialReg.Rd +++ /dev/null @@ -1,39 +0,0 @@ - -\name{model.svmRadialReg} -\alias{model.svmRadialReg} -\alias{svmRadialReg} -\docType{model.svmRadialReg} - -\title{ -Specifications needed for Support Vector machines wit Radial basis function kernel regression model -} - -\description{ -A list of parameters needed for SVM radial kernel regression model -} - -\usage{data("model.svmRadialReg")} - -\format{A list including all parameters needed for svm regression model in R caret package and specification for parameter space search, specifically: -\describe{ -\item{label}{a character string specifying the model} -\item{library}{a character string 'kernlab' to specify the library used} -\item{type}{a charcater vector c('Regression') for SVMRFEreg function to specify the problem studied} -\item{parameters}{a 3x3 data frame of characters specifying the parameters used, namely sigma, C and epsilon, their class and label} -\item{grid}{a grid function to specify the grid of the parameter spece for sigma; arguments are x (...), y (..) and len (length of C)} -\item{loop}{NULL} -\item{fit}{a function to specify fitting model} -\item{predict}{a function to specify prediction model} -\tem{prob}{prediction probabilities} -\item{predictors}{a function to generate predictions} -\item{tags}{a character vector including all possible models} -\item{levels}{a function for S4 VIRTUAL class objects from kernlab R library} -\item{sort}{a sort function used for parameters sigma, C, epsilon} -} -} -\examples{ -data(model.svmRadialReg) - -svmRadialReg -} -\author{Jose A. Seoane, Carlos Fernandez-Lozano}