Calling R object from R function

Hi,

I edit my post, Can you please help with this matter?

Hi,

Can you please help me with this please?

What I am trying to do is call a vector from R function and used in the new
function
# 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))
colnames(Dat) <- c("a0","a","b","c0","c1","c2","c3","c4","c5","Y")

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)
}


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)






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