[问题类型]: 效能谘询(我想让R 跑更快) [软体熟悉度]: 使用者(已经有用R 做过不少作品) [问题叙述]: 各位好，目前我正在写一支配Stepwise Regression的程式；且已经可以成功执行。 但是在执行效能上还有很大的进步空间 目前所配的Variables 共有80左右，所以配逐步回归的方式来选取留下的Variable 因为每个变数都需要建立一个独立模型 所以总共会跑80次左右的iterstions 总共执行时间约莫落在2.5HR左右，所以开始考虑效能提升问题 有遇到一些问题需要各位协助排除障碍，先在此谢谢各位 以下的范例，我先设定跑2次的iteration用来测试效能 [程式范例]: 这是我已经写好可以Run的程式码 ##########可以Run的########### system.time(lapply(1:2,function(i)( { print(i) TrainModel<-cbind(setnames(TrainDT[7:nrow(TrainDT),i,with=F],paste0(names(DT[1,i,with=FALSE]),'_y')),TrainDT[1:(nrow(TrainDT)-6),1:length(TrainDT),with=F]) PracticeModel<-cbind(setnames(PracticeDT[7:nrow(PracticeDT),i,with=F],paste0(names(DT[1,i,with=FALSE]),'_y')),PracticeDT[1:(nrow(PracticeDT)-6),1:length(PracticeDT),with=F]) resp<-grep('_y',names(TrainModel),value=T) pre<-grep('01F',names(TrainModel),value =T) pre<-pre[2:length(pre)] addq<-function(x) paste0("`",x, "`") Model<-as.formula(paste(addq(resp),paste(lapply(pre, addq),collapse = '+'),sep = '~')) FitModel<-lm(Model,data=TrainModel) #Fitmodel<-lm(`01F0017S_y`~.,data=TrainModel) #Fitmodel<-lm(as.matrix(TrainDT[7:nrow(TrainDT),i,with=F])~as.matrix(TrainDT[1:(nrow(TrainDT)-6),1:length(TrainDT),with=F]),data=TrainDT) stepwise<-step(FitModel,sacle=0,direction = 'both') write.csv(stepwise$coefficients,file = paste0(names(DT[1,i,with=FALSE]),'_Coefficients','.csv')) write.csv(cbind(TrainModel[[1]],stepwise$fitted.values,stepwise$residuals),file = paste0(names(DT[1,i,with=FALSE]),'_Residual','.csv')) write.csv(cbind(PracticeModel[[1]],predict(stepwise,PracticeModel),PracticeModel[[1]]-predict(stepwise,PracticeModel)),file = paste0(names(DT[1,i,with=FALSE]),'_Predict','.csv')) #PredictData<-predict(stepwise,PracticeDT) }))) 每一个iteration在最後会丢出三个我需要参数的csv，总共耗时约2.5HR ###########配合snow 套件的程式########### clusterfun<-function(i){ print(i) TrainModel<-cbind(setnames(TrainDT[7:nrow(TrainDT),i,with=F],paste0(names(DT[1,i,with=FALSE]),'_y')),TrainDT[1:(nrow(TrainDT)-6),1:length(TrainDT),with=F]) PracticeModel<-cbind(setnames(PracticeDT[7:nrow(PracticeDT),i,with=F],paste0(names(DT[1,i,with=FALSE]),'_y')),PracticeDT[1:(nrow(PracticeDT)-6),1:length(PracticeDT),with=F]) resp<-grep('_y',names(TrainModel),value=T) pre<-grep('01F',names(TrainModel),value =T) pre<-pre[2:length(pre)] addq<-function(x) paste0("`",x, "`") Model<-as.formula(paste(addq(resp),paste(lapply(pre, addq),collapse = '+'),sep = '~')) FitModel<-lm(Model,data=TrainModel) #Fitmodel<-lm(`01F0017S_y`~.,data=TrainModel) #Fitmodel<-lm(as.matrix(TrainDT[7:nrow(TrainDT),i,with=F])~as.matrix(TrainDT[1:(nrow(TrainDT)-6),1:length(TrainDT),with=F]),data=TrainDT) stepwise<-step(FitModel,sacle=0,direction = 'both') write.csv(stepwise$coefficients,file = paste0(names(DT[1,i,with=FALSE]),'_Coefficients','.csv')) write.csv(cbind(TrainModel[[1]],stepwise$fitted.values,stepwise$residuals),file = paste0(names(DT[1,i,with=FALSE]),'_Residual','.csv')) write.csv(cbind(PracticeModel[[1]],predict(stepwise,PracticeModel),PracticeModel[[1]]-predict(stepwise,PracticeModel)),file = paste0(names(DT[1,i,with=FALSE]),'_Predict','.csv')) #PredictData<-predict(stepwise,PracticeDT) } cluster <- makeCluster(type="SOCK",c("localhost", "localhost", "localhost", "localhost")) system.time(parLapply(cluster,1:2,clusterfun)) stopCluster(cluster) 但是在执行上得到这个错误 > cluster <- makeCluster(type="SOCK",c("localhost", "localhost", "localhost", "localhost")) > system.time(parLapply(cluster,1:2,clusterfun)) Error in checkForRemoteErrors(val) : 2 nodes produced errors; first error: 没有这个函数 "setnames" Timing stopped at: 0 0 0.01 > stopCluster(cluster) 请问各位该如何排除这样的障碍，谢谢各位的指教 [环境叙述]: > version _ platform x86_64-w64-mingw32 arch x86_64 os mingw32 system x86_64, mingw32 status major 3 minor 2.1 year 2015 month 06 day 18 svn rev 68531 language R version.string R version 3.2.1 (2015-06-18) nickname World-Famous Astronaut [关键字]: 选择性，也许未来有用 Snow, data.table,Stepwise Regression --

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