R help - May 2011 - Help with PLSR with jack knife

Hi

I am analysing a dataset of 40 samples each with 90,000 intensity measures for 
various peptides. I am trying to identify the Biomarkers (i.e. most significant 
peptides). I beleive that PLS with jack knifing, or alternativeley 
CMV(cross-model-validation) are multivariateThe 40 samples belong to four 
different groups. 


I have managed to conduct the plsr using the commands:

BHPLS1 <- plsr(GroupingList ~ PCIList, ncomp = 10, data = PLSdata, validation
=
"LOO")

and

BHPLS1 <- plsr(GroupingList ~ PCIList, ncomp = 10, data = PLSdata, validation
=
"CV")

I have also used the following command to obtain the loadings

BHPLS1_Loadings <- loadings(BHPLS1)

Now I am unsure of how to utilise these to identify the significant variables. 
Do I need to use any loops?

str(BHPLS1_Loadings) 
 loadings [1:94727, 1:10] -0.00113 -0.03001 -0.00059 -0.00734 -0.02969 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:94727] "PCIList1" "PCIList2"
"PCIList3" "PCIList4" ...
  ..$ : chr [1:10] "Comp 1" "Comp 2" "Comp 3"
"Comp 4" ...
 - attr(*, "explvar")= Named num [1:10] 14.57 6.62 7.59 5.91 3.26 ...
  ..- attr(*, "names")= chr [1:10] "Comp 1" "Comp
2" "Comp 3" "Comp 4" ...


Many thanks in advance
AK

	[[alternative HTML version deleted]]

Amit Patel <amitrhelp at yahoo.co.uk> writes:
> BHPLS1 <- plsr(GroupingList ~ PCIList, ncomp = 10, data = PLSdata,
validation =
> "LOO")
>
> and
>
> BHPLS1 <- plsr(GroupingList ~ PCIList, ncomp = 10, data = PLSdata,
validation =
> "CV")
[...]
> Now I am unsure of how to utilise these to identify the significant
variables.
You can use the jackknife built into plsr to get an indication about
significant variables, by adding the argument "jackknife = TRUE" to
the
plsr call.  Use jack.test(BHPLS1) to do the test.

But _PLEASE_ do read the "Warning" section inf ?jack.test!

-- 
Regards,
Bj?rn-Helge Mevik