[R] PROC DISCRIM vs. lda( ) in MASS

[Greg Tarpinian]

Hello,

I am using WinXP, R version 2.3.1, and SAS for PC version 8.1.

I have mostly used SAS over the last 4 years and would like to
compare the output of PROC DISCRIM to that of lda( ) with respect
to a very specific aspect.  My data have k=3 populations and there
are 3 variates in the feature space.  When using using the code

   PROC DISCRIM DATA = FOO OUT = FOO_OUT OUTSTAT = FOOSTAT
                METHOD = NORMAL LIST POOL = YES PCOV MANOVA;
     CLASS STRATA;
     PRIORS EQUAL;
     VAR X1 X2 X3;
   RUN;

I am able to easily obtain the linear discriminant functions for
the strata which allow computation of the three discriminant
scores for a given observation.  This information is contained 
in WORK.FOOTSTAT and may be extracted by subsetting:

   DATA LDFUNC;
     SET FOOSTAT(WHERE = (_TYPE_ = LINEAR));
   RUN;

To actually implement the linear discriminant functions takes
a bit more formatting, but there it is.

My question:  Where is this information stored in R?  I completely
understand that predict( ) or predict.lda( ) are the preferable
ways to obtain a classification prediction for new observations.
I still want to "see" the individual lin. discrim. functions and
work with them myself.  I have been using

   x.lda <- lda(Strata ~ X1 + X2+ X3, data = foo.frame)

to construct the analysis.


Much thanks,

    Greg