R help - Oct 2012 - data structure for plsr

I am having a similar problem understanding the data structure of the
"yarn" dataset described in the "[R] data structure for
plsr" posts. I have
spectroscopic data I'd like to run through a PLSR and have read the
tutorial series, but still do not understand the data format required for
the code to process my data. My current data structure consists of a .csv
file read into R containing 15 columns (a charcoal dilution series going
from 100% to 0%) and 1050 rows of absorbance data from 400 nm to 2500 nm at
 2 nm interval. I think I need to transpose the data such that the specific
wavelengths become my columns and dilutions are defined in rows, but after
that point I am lost. Should I (and how do I) make my absorbance data into
individual matrices that read into a data frame with only two columns
(dilution vs spectra or something of the sort that both the yarn and
gasoline data use)? Thank you for any pointers.

Emma

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Emma Jones <evjones at ualberta.ca> writes:
> My current data structure consists of a .csv
> file read into R containing 15 columns (a charcoal dilution series going
>>From 100% to 0%) and 1050 rows of absorbance data from 400 nm to 2500 nm
at
>  2 nm interval. I think I need to transpose the data such that the specific
> wavelengths become my columns and dilutions are defined in rows,
Yes, you need to transpose the data so a coloumn corresponds to a
variable (response or predictor).
> Should I (and how do I) make my absorbance data into
> individual matrices that read into a data frame with only two columns
It is best to put all predictors (wavelengths) together in one matrix,
yes.  The same for the responses, if you have more than one response
coloumn.

This is untested, so there might be errors:

Assuming that your spectroscopic data is read into a data frame called
origspec:

## This should create a matrix with the wavelengths as coloumns:
spec <- t(as.matrix(origspec))

I don't know what your response is, so I'm just assuming it is in a
vector called resp.

# This would create a data frame suitable for plsr():
mydata <- data.frame(resp = resp, spec = I(spec))

Then you can analyse like this:

plsr(resp ~ spec, data = mydata, ....)

-- 
Bj?rn-Helge Mevik