reiterating a script using r - r

Question

I have the following script
Posdef <- function (n, ev = runif(n, 0, 10))
{
Z <- matrix(ncol=n, rnorm(n^2))
decomp <- qr(Z)
Q <- qr.Q(decomp)
R <- qr.R(decomp)
d <- diag(R)
ph <- d / abs(d)
O <- Q %*% diag(ph)
Z <- t(O) %*% diag(ev) %*% O
return(Z)
}
Sigma <- Posdef(n = 11)
mu <- runif(11,0,10)
data <- as.data.frame(mvrnorm(n=1000, mu, Sigma))
data[data < 0] <- 0 #setting a floor#
data[data > 10] <- 10 #setting a ceiling#
names(data) = c('criteria_1', 'criteria_2', 'criteria_3', 'criteria_4', 'criteria_5',
'criteria_6', 'criteria_7', 'criteria_8', 'criteria_9', 'criteria_10',
'outcome')
data$outcome <- ifelse(data$outcome > 5, 1, 0)
data <- data[, sapply(data, is.numeric)]
maxValue <- as.numeric(apply (data, 2, max))
minValue <- as.numeric(apply (data, 2, min))
data_scaled <- as.data.frame(scale(data, center = minValue,
scale = maxValue-minValue))
ind <- sample (1:nrow(data_scaled), 600)
train <- data_scaled[ind,]
test <- data_scaled[-ind,]
model <- glm (formula =
outcome ~ criteria_1 + criteria_2 + criteria_3 + criteria_4 + criteria_5 +
criteria_6 + criteria_7 + criteria_8 + criteria_9 + criteria_10,
family = "binomial",
data = train)
summary (model)
predicted_model <- predict(model, test)
neural_model <- neuralnet(formula =
outcome ~ criteria_1 + criteria_2 + criteria_3 + criteria_4 + criteria_5 +
criteria_6 + criteria_7 + criteria_8 + criteria_9 + criteria_10,
hidden = c(2,2) ,
threshold = 0.01,
stepmax = 1e+07,
startweights = NULL,
rep = 1,
learningrate = NULL,
algorithm = "rprop+",
linear.output=FALSE,
data= train)
plot (neural_model)
results <- compute (neural_model, test[1:10])
results <- results$net.result*(max(data$outcome)-
min(data$outcome))+ min(data$outcome)
Values <- (test$outcome)*(max(data$outcome)-
min(data$outcome)) + min(data$outcome)
MSE_nueral_model <- sum((results - Values)^2)/nrow(test)
MSE_model <- sum((predicted_model - test$outcome)^2)/nrow(test)
print(MSE_model - MSE_nueral_model)
R1 <- (MSE_model - MSE_nueral_model)
The purpose of this script is to generate some arbitrary multivariate distribution and then compare two methods. In this case its a neural net and logistic regression. The end result is a difference in mean square error.
Now my issue with creating a loop has been with generating the 1000 observations.
I am able to create a loop without the data simulation portion of the script, putting that into the loop seems to make things go haywire. I tried creating a column vector filled with NA's but all I ended up getting was a single value returned rather than a vector of length n populated by the MSE reductions for each iteration of the loop.
Any help would be greatly appreciated.