[R] PLS regression on near infrared (NIR) spectra data

[Mark Difford]

Hi Paulo,

You might also want to look at something like the glmnet package (Friedman,
Hastie, and Tibshirani). This carries out penalized regression, is designed
to work with high numbers of predictors/inputs/columns and relatively few
samples/obervations/rows, and is very fast.

See: http://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf

HTH, Mark.



Paulo Ricardo Gherardi Hein wrote:
> 
> Dear collegues,
> 
> I´ ve worked with near infrared (NIR) spectroscopy to assess chemical,
> physical, mechanical and anatomical properties of wood.
> 
> I use "The Unscrambler" software to correlate the matrix of dependent
> variables (Y) with the matrix of spectral data (X) and I would like to
> migrate to R. The matrix of spectral variables is very large (2345 columns
> and n lines, where n = samples), so we used Partial Least Squares
> Regression
> to predict a variable y (content of cellulose, for instance) based on the
> spectral variables, which are the NIR wavelengths.
> 
> I am new here (since jan2009) and up to now, I not seen anyone commenting
> about principal component analysis and regression PLS to analyze spectral
> information in R system. Sorry, I am a R starter...
> 
> Anybody have any package, or trick to suggest me?
> 
> Grateful for yours information!
>  --
> Paulo Ricardo Gherardi Hein
> PhD candidate at University of Montpellier 2
> CIRAD - PERSYST Department
> Research unit: Production and Processing of Tropical Woods - TA B-40/16
> 73 rue Jean-François Breton 34398 Montpellier Cedex 5, France
> phone: +33 4 67 61 44 51
> skype: paulo_hein
> email: paulo.hein at cirad.fr
> 
> 	[[alternative HTML version deleted]]
> 
> 
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> 

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