[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.

```#################################################
## FIT A MULTIVARIATE ADAPTIVE REGRESSION      ##
## SPLINES MODEL (MARS) USING MDA PACKAGE      ##
## DEVELOPED BY HASTIE AND TIBSHIRANI          ##
#################################################

# LOAD LIBRARIES AND DATA
library(MASS);
library(mda);
data(Boston);

# FIT AN ADDITIVE MARS MODEL
mars.fit <- mars(Boston[, -14], Boston[14], degree = 1, prune = TRUE, forward.step = TRUE)

# SHOW CUT POINTS OF MARS
cuts <- mars.fit\$cuts[mars.fit\$selected.terms, ];
dimnames(cuts) <- list(NULL, names(Boston)[-14]);
print(cuts);

factor <- mars.fit\$factor[mars.fit\$selected.terms, ];
dimnames(factor) <- list(NULL, names(Boston)[-14]);
print(factor);

# EXAMINE THE FITTED FUNCTION BETWEEN EACH IV AND DV
par(mfrow = c(3, 5), mar=c(2, 2, 2, 2), pty="s")
for (i in 1:13)
{
xp <- matrix(sapply(Boston[1:13], mean), nrow(Boston), ncol(Boston) - 1, byrow = TRUE);
xr <- sapply(Boston, range);
xp[, i] <- seq(xr[1, i], xr[2, i], len=nrow(Boston));
xf <- predict(mars.fit, xp);
plot(xp[, i], xf, xlab = names(Boston)[i], ylab = "", type = "l");
}
```

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)