[This article was first published on Yet Another Blog in Statistical Computing » S+/R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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


To leave a comment for the author, please follow the link and comment on their blog: Yet Another Blog in Statistical Computing » S+/R.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

# Never miss an update! Subscribe to R-bloggers to receive e-mails with the latest R posts.(You will not see this message again.)

Click here to close (This popup will not appear again)