While coding ensemble methods in data mining with R, e.g. bagging, we often need to generate many data and models objects with sequential names. Below is a quick example how to use assign() function to generate many prediction objects on the fly and then retrieve these predictions with mget() to do the model averaging.

data(Boston, package = "MASS")

for (i in 1:10) {
  set.seed(i)
  smp <- Boston[sample(1:nrow(Boston), nrow(Boston), replace = TRUE), ]
  glm <- glm(medv ~ ., data = smp)
  prd <- predict(glm, Boston)
  ### ASSIGN A LIST OF SEQUENTIAL NAMES TO PREDICTIONS ###
  assign(paste("p", i, sep = ""), prd)
}

### RETURN NAMED OBJECTS TO A LIST ###
plist <- mget(paste('p', 1:i, sep = ''))
### AGGREGATE ALL PREDICTIONS ###
pcols <- do.call('cbind', plist)
pred_medv <- rowSums(pcols) / i

### A SIMPLE FUNCTION CALCULATION R-SQUARE ###
r2 <- function(y, yhat) {
  ybar <- mean(y)
  r2 <- sum((yhat - ybar) ^ 2) / sum((y - ybar) ^ 2)
  return(r2)
}
print(r2(Boston$medv, pred_medv))
# OUTPUT:
# [1] 0.7454225