On Thu, Nov 8, 2012 at 2:44 PM, Fares Said <frespi...@hotmail.com> wrote: > Hi Michael, > > you mentioned I can get help, can you give more details please.
Hi Fares, I also asked that you keep our correspondance on the mailing list. Please do so. If you want private help, you can seek a local statistical consultancy, but free help is available here, if you follow protocol. > > I also edited the example to make productive. But I'm not using Nabble and neither are most of your other potential respondents: if you want to use an email help list, it's much better to use email. For instance, if we check the official R help archives -- https://stat.ethz.ch/pipermail/r-help/2012-November/328391.html -- you'll see your post wasn't changed. It's rather wretched that Nabble allows that, in my opinion, as it invalidates conversations. > > and you might tell me to create the M1 function on the get.m function but I > prefer not because I have 9 to ten function and they have lots statement and > I prefer to keep them separate > I've copied your Nabble post here: ------------------------------------------------------- a0=rep(1,40) a=rep(0:1,20) b=c(rep(1,20),rep(0,20)) c0=c(rep(0,12),rep(1,28)) c1=c(rep(1,5),rep(0,35)) c2=c(rep(1,8),rep(0,32)) c3=c(rep(1,23),rep(0,17)) c4=c(rep(1,6),rep(0,34)) x=matrix(cbind(a0,a,b,c0,c1,c2,c3,c4,rnorm(40)),nrow=40,ncol=9) Dat <- cbind(x,rnorm(40,2,30));colnames(Dat) <- c("a0","a","b","c0","c1","c2","c3","c4","c5","Y") M1 <- function(Trdat,Tedat,mdat,nsam,conv){ M1list <- NULL vectx <- c(1,4,6,7,9) vectz <- c(2,3,7,1,4,9) X <- Trdat[,vectx] Z <- Trdat[,vectz] Y <- Trdat[,ncol(Trdat)] TesX <- Tedat[,vectx] TesZ <- Tedat[,vectz] TesY <- Tedat[,ncol(Tedat)] Treig <- eigen(crossprod(X))$values if(any(abs(Treig) < conv))stop("In M1 the design matrix (X) is singular for simulation ",paste(nsam)) Comp <- c("nCol(X)"= ncol(X),"nCol(Z)"= ncol(Z),"Is length(Y)=nrow(X)"= length(Y)==nrow(X), "Is length(Y)=nrow(Z)"= length(Y)==nrow(Z)) M1list$vectx <- vectx M1list$vectz <- vectz M1list$X <- X M1list$Z <- Z M1list$Y <- Y M1list$TesX <- TesX M1list$TesZ <- TesZ M1list$TesY <- TesY M1list$Comp <- Comp return(M1list) } get.m <- function(dat,asim,ModelFun,M,conv){ Sim <- list() modInd <- ModelFun(Trdat=dat,Tedat=dat,mdat=dat,nsam=-1,conv=conv) # HERE WHERE I NEED HELP i only need to import vectx and vectz that is why I set Trdat=Tedat=dat if(M==1){ vecx <- modInd$vectx vecz <- modInd$vectz px <- length(vecx) pz <- length(vecz) pk <- length(modInd$Comp) nam <-colnames(dat[,vecx]) Asse <- matrix(NA,nrow=asim,ncol=px) Check <- matrix(NA,nrow=pk,ncol=asim) colnames(Check) <- paste("CheckIter",1:asim,sep="") } else { vecx <- modInd$vectx vecz <- modInd$vectz px <- length(vecx) pz <- length(vecz) pk <- length(modInd$Comp) nam <-colnames(dat[,vecx]) Asse <- matrix(NA,nrow=asim,ncol=px) Check <- matrix(NA,nrow=pk,ncol=asim) colnames(Check) <- paste("CheckIter",1:asim,sep="") } for(k in 1:asim){ cat("Iter #",paste(k),"\n") #========================================================================================== # Start Sampling code #========================================================================================== # Sample the Index for Train Set set.seed(k) Indx<-sample(1:nrow(dat),nrow(dat),replace=T) SamDat <- dat[Indx,] # Split Data set.seed(k) TrainInd <- sample(1:nrow(SamDat), trunc(2*length(1:nrow(SamDat))/3)) # Sample 2/3 of the data TrSet <- SamDat[TrainInd,] # Train data ######## Hold 1/3 of the data TeSet <- SamDat[-TrainInd,] # hold 1/3 of the data Trind <- ceiling((2*length(Indx))/3) Model <- ModelFun(Trdat=TrSet,Tedat=TeSet,mdat=dat,nsam=k,conv=conv) Y <- Model$Y X <- Model$X Z <- Model$Z TesX <- Model$TesX TesZ <- Model$TesZ TesY <- Model$TesY xnam <-colnames(X) znam <-colnames(Z) pc <- ncol(X) fmla <- as.formula(paste("Y ~",paste(xnam, collapse= "+"),"-1",sep="")) fitlm <- lm(formula=fmla,data = data.frame(cbind(X,Y))) ResiSqr <- (residuals(fitlm))*(residuals(fitlm)) Check[,k] <- Model$Comp Asse[k,1:pc] <- coef(fitlm) } Sim$Check <- Check Sim$Asse <- Asse return(Asse) } get.m(dat=Dat,asim=6,ModelFun=M1,M=1,conv=1e-4) ------------------------------------------------------ Your code is quite messy and difficult to follow: I'd write it as follows: # Initialize some data Dat <- cbind( a0 = rep(1, 40), a = rep(0:1, 20), b = rep(c(1,0), each = 20), c0=c(rep(0,12),rep(1,28)), c1=c(rep(1,5),rep(0,35)), c2=c(rep(1,8),rep(0,32)), c3=c(rep(1,23),rep(0,17)), c4=c(rep(1,6),rep(0,34)), Y = rnorm(40,2,30)) M1 <- function(Trdat,Tedat,mdat,nsam,conv){ vectx <- c(1,4,6,7,9) vectz <- c(2,3,7,1,4,9) X <- Trdat[,vectx] Z <- Trdat[,vectz] Y <- Trdat[,ncol(Trdat)] TesX <- Tedat[,vectx] TesZ <- Tedat[,vectz] TesY <- Tedat[,ncol(Tedat)] Treig <- eigen(crossprod(X))$values if(any(abs(Treig) < conv)) stop("In M1 the design matrix (X) is singular for simulation ", nsam) Comp <- c(`nCol(X)`= ncol(X),`nCol(Z)`= ncol(Z), `Is length(Y)=nrow(X)` = length(Y)==nrow(X), `Is length(Y)=nrow(Z)` = length(Y)==nrow(Z)) list(vectx = vectx, vectz = vectz, X = X, Z = Z, Y = Y, TesX = TesX, TesZ = TesZ, TesY = TesY, Comp = Comp) } with similar modifications throughout the rest of your code. Hopefully you find that easier to read: I certainly do. Now, it's still not entirely clear to me what you're asking: but perhaps global assignment and/or lexical scoping does what you want? Consider these tricks: ######################### add_magic_Z <- function(z){ Z <- z function(x) x + z } add_2 <- function(2) add_2(5) # 7 add_3 <- function(3) add_3(0) # 3 ########################## f <- function(x) { X <<- X + x return(x^2) } X <- -3 f(5) f(10) print(X) # increased by the `<<-` inside of f() ########################### The latter is considered bad programming practice however. Cheers, Michael ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
