因此,到目前为止,我只编写了少数 R 代码(准确地说是 2 个项目),这可能证明了这个问题对于经验丰富的程序员来说是多么愚蠢。
我正在尝试并行化我的 K 折交叉验证代码,该代码旨在找到用于最终模型的最佳变量集。
代码有点像这样
child <- foreach(i=icount(ncol(parentModel)-1),.combine = 'rbind') %:%{
childModel<-parentModel
childModel[,i]<-NULL
filteredTestMTM <-foreach(j = icount(nFolds),.combine = c, .export = c("DataSplit","getProbabilityThreshold","SharpeRatio")) %dopar% {
splitData <- DataSplit(childModel, nFolds = nFolds, testFold=j)
testData<-splitData$testData
trainingData<-splitData$trainingData
trainingMTM <- trainingData[,ncol(trainingData)]
testMTM <- testData[,ncol(testData)]
Trade <- (trainingMTM > 0.001)*1.0 #mtmThreshold to be used here instead of 0.001
trainingData <- trainingData[,1:(ncol(trainingData)-1),drop=FALSE]
trainingData <- cbind(trainingData, Trade)
logmodel <- glm(Trade ~ .,data=trainingData, family = "binomial"(link="logit") )
trainingData <- trainingData[,1:(ncol(trainingData)-1),drop=FALSE]
trainingResults <- predict(logmodel, newdata=trainingData, type="response")
probabilityThreshold <- getProbabilityThreshold(trainingResults, trainingMTM, 0.001) #new Probability function to be defined to use optimParam
tR <- predict(logmodel, newdata=testData, type="response")
tMTM <- testMTM * ((tR>probabilityThreshold)*1.0)
return(tMTM)
}
totalSharpe <- (mean(filteredTestMTM)/sd(filteredTestMTM))
if (is.nan(totalSharpe)) {
totalSharpe = 0.0
}
return(c(totalSharpe,i ))
}
综上所述——我取parentModel,一一去掉变量,运行K折交叉验证,收集结果。但是我不断收到错误
Error in `[<-.data.frame`(`*tmp*`, , i, value = NULL) :
object 'i' not found
有人可以帮我吗?
编辑:我在 Windows 7 上。