R help - Jan 2008 - forming a linear discriminant function from the output of lda()

Hello all-

I am a relatively new user of R and am working through a graduate course
in
Statistics that uses Minitab, SAS and some Matlab.  I like using R but
am 
having some trouble lining up the output from lda() to that of the other
programs'
results.  The dataset below is a modified set of wine data from the
Pinot Noir
data set as an illustration of the 2 group LDA scenario.

	Mo    Ba Region
1  0.058 0.225      3
2  0.071 0.105      3
3  0.147 0.301      2
4  0.116 0.166      3
5  0.166 0.132      3
6  0.261 0.078      3
7  0.191 0.085      3
8  0.009 0.072      3
9  0.027 0.094      3
10 0.030 0.349      2
11 0.268 0.099      3
12 0.245 0.071      3
13 0.161 0.181      2
14 0.146 0.328      2
15 0.155 0.081      3
16 0.126 0.299      2
17 0.211 0.206      2
18 0.129 0.281      2
19 0.166 0.292      2
20 0.199 0.292      2
21 0.208 0.087      3

There are various displays from SAS, Minitab and Matlab's Statistics
Toolbox, but
they all agree with the final linear discriminant function: 0 = -19.51 +
21.47*Mo + 84.08*Ba.

When I run the following code in R: 
> library(MASS)
> wine.lda <- lda(Region ~ Mo + Ba, data = wine, prior = c(1,1)/2)
> wine.lda
Call:
lda(Region ~ Mo + Ba, data = wine, prior = c(1, 1)/2)

Prior probabilities of groups:
  2   3 
0.5 0.5 

Group means:
         Mo        Ba
2 0.1461111 0.2810000
3 0.1479167 0.1079167

Coefficients of linear discriminants:
          LD1
Mo  -5.636024
Ba -22.069187

I am having trouble going from the cofficients derived from the
spherical within group covariance to the
function I am able to obtain in the other programs.

The rest of the problem set up for all programs are: Region = group
assignment, prior = equal priors and 
I do not do any data pretreatment prior to analysis.

Any help would be great.

Thanks,

Matt

	[[alternative HTML version deleted]]