This video shows how to obtain and install R on the Mac OS X platform. It also shows a few basic functions in R, such as how to install packages in R and load them for use. A PC version will follow shortly

## R-Bloggers Steadily Growing

## In case you missed it: January Roundup

In case you missed them, here are some articles from January of particular interest to R users. Sponsorships from Revolution Analytics are now available for local R user groups in 2012. The winners of the Applications of R in Business Contest have been announced. The coefplot package visualizes model coefficients and standard errors in a line chart. Revolution Analytics...

## "R" PLS Package: Multiple Scatter Correction (MSC)

MSC (Multiple Scatter Correction) is a Math treatment to correct the scatter in the spectra. The scatter is produced for different physical circumstances as particle size, packaging.Normally scatter make worse the correlation of the spectra with the constituent of interest.Almost all the chemometric software’s available include this math treatment and of course “R” have it as well in the...

## "R": Predicting a Test Set (Gasoline)

## "R": PLS Regression (Gasoline) – 005

## "R": PLS Regression (Gasoline) – 004

In the previous post we plot the Cross Validation predictions with:> plot(gas1, ncomp = 3, asp = 1, line = TRUE)We can plot the fitted values instead with:> plot(gas1, ncomp = 3, asp = 1, line = TRUE,which=train) Graphics are different:Of course, using "train" we get overoptimisc statistics and we should look...

## "R": PLS Regression (Gasoline) – 003

The gasoline data set has the spectra of 60 samples acquired by diffuse reflectance from 900 to 1700 nm. We saw how to plot the spectra in the previous post.Now, following the tutorial of Bjorn-Helge Mevik published in "R-News Volume 6/3, August 2006", we will do the PLS regression:gas1 <- plsr(octane~NIR, ncomp = 10,data = gasoline, validation...

## "R": Looking at the Data (Gasoline) – 001

As other softwares "R" has nice tools to look to the data before to develop the calibration.Statistics for the "Y" variable (in this case octane number) like Maximun, Minimun,..,standard deviation,...are important:> library(ChemometricsWithR)> data(gasoline)> summary(gasoline$octane) Min. 1st Qu. Median Mean 3rd Qu. Max. 83.40 85.88 87.75 87.18 88.45 89.60> sd(gasoline$octane) 1.530078And of course the Histogram:> hist(gasoline$octane)