how to get output from data frames in R code - r

Question

I have the following code to analyze data sets:
library("Matrix")
Data <-list(c(2,3),c(3,2),c(2,2))
TheSizes=c(3,4)
n=2
dd=2
StdGrid <- function(Data,TheSizes)
{
SGrid <- list(
Values = Data,
Sizes = TheSizes
)
class(SGrid) <- append(class(SGrid), c("StdGrid","Moment"))
return(SGrid)
}
theObject=StdGrid
MHistogramC <- function(theObject,n,dd)
{
sizes <- theObject$Sizes
l <- length(sizes)
data <- theObject$Values
Xarray <- matrix(rep(0,l*n),ncol=n)
N <- matrix(rep(0,l*n),ncol=n)
Histo <- matrix(rep(0,l*n),ncol=n)
GrandX <- lapply(data,function(x) log(x))
minX <- rep(0,l)
maxX <- rep(0,l)
DeltaX <- rep(0,l)
for(i in 1:l){
minX[i] <- min(GrandX[[i]])
maxX[i] <- max(GrandX[[i]])
DeltaX[i] <- maxX[i]/n-minX[i]/n
}
nzero <- numeric()
for(j in 1:n){
for(i in 1:l){
Xarray[i,j] <- minX[i]+(j-1/2)*DeltaX[i]
N[i,j] <- length(which((GrandX[[i]] >= minX[i]+(j-1/2)*DeltaX[i]-DeltaX[i]) & (GrandX[[i]] <= minX[i]+(j-1/2)*DeltaX[i]+DeltaX[i])))
Histo[i,j] <- log(N[i,j])
}
if(min(Histo[,j]) > - 10000){
nzero <- c(nzero,j)
}
}
alpha <- rep(0,lnzero)
falpha <- rep(0,lnzero)
for(j in 1:length(nzero)){
fit <- lm(Xarray[,nzero[j]] ~ log(sizes/dd))
alpha[j] <- fit$coefficients[[2]]
fit2 <- lm(Histo[,nzero[j]] ~ log(sizes/dd))
falpha[j] <- -fit2$coefficients[[2]]
}
Result <- data.frame(alpha=alpha,falpha=falpha)
return(Result)
}
MHistogramU <- function(theObject,n,dd)
{
sizes <- theObject$Sizes
l <- length(sizes)
data <- theObject$Values
Xarray <- matrix(rep(0,l*n),ncol=n)
N <- matrix(rep(0,l*n),ncol=n)
Histo <- matrix(rep(0,l*n),ncol=n)
GrandX <- lapply(data,function(x) log(x))
minX <- rep(0,l)
maxX <- rep(0,l)
DeltaX <- rep(0,l)
for(i in 1:l){
minX[i] <- min(GrandX[[i]])
maxX[i] <- max(GrandX[[i]])
DeltaX[i] <- maxX[i]/n-minX[i]/n
}
nzero <- numeric()
for(j in 1:n){
for(i in 1:l){
Xarray[i,j] <- minX[i]+(j-1/2)*DeltaX[i]
N[i,j] <- length(which((GrandX[[i]] >= minX[i]+(j-1/2)*DeltaX[i]-sqrt(DeltaX[i])) & (GrandX[[i]] <= minX[i]+(j-1/2)*DeltaX[i]+sqrt(DeltaX[i]))))
Histo[i,j] <- log(N[i,j])
}
if(min(Histo[,j]) > - 10000){
nzero <- c(nzero,j)
}
}
alpha <- rep(0,lnzero)
falpha <- rep(0,lnzero)
for(j in 1:length(nzero)){
fit <- lm(Xarray[,nzero[j]] ~ log(sizes/dd))
alpha[j] <- fit$coefficients[[2]]
fit2 <- lm(Histo[,nzero[j]] ~ log(sizes/dd))
falpha[j] <- -fit2$coefficients[[2]]
}
Result <- data.frame(alpha=alpha,falpha=falpha)
return(Result)
}
Which compiles, but i don't get anything in return. If I try to print "Result" the console says that the object "Result" was not found.
The inputs are:
Data : is a list of vector/grids
TheSizes : is a vector
theObject : the data defined as the class 'StdGrid' (defined below);
n : the number of values of alpha to be calculated;
dd : the dimension of the physical support of the measure.
What can I do to see the data frame that the code is supposed to return?

Answer 1

That is because your code is just a bunch of functions which are not called at all. A function would return value only when the function is called, it won't call itself.
Now looking at your code, It's hard to deduce what you are trying to calculate/analyze, but assuming all other codes are correct, and all functions are coded perfectly, you need to add the following lines to view the result at the end of your code:
var_MHistogramU <- MHistogramU(theObject,n,dd)
var_MHistogramC <- MHistogramC(theObject,n,dd)
To view the result, simply print the variables.
print(var_MHistogramU)
print(var_MHistogramC)
Remember: Printing Result won't work as the variable result is a local variable for the function, which is inaccessible globally.