[R] Dataframes in PLS package

[westland]

I have been working with the pls procedure and have problems getting the
procedure to work with matrix or frame data.  I suspect the problem lies in
my understanding of frames, but can't find anything in the documentation
that will help. 

Here is what I have done:

I read in an 10000 x 8 table of data, and assign the first four columns to
matrix A and the second four to matrix B

pls <-    read.table("C:/Users/Chris/Desktop/SEM Book/SEM Stat
Example/Simple Header Data for SEM.csv",    header=TRUE, sep=",",
na.strings="NA", dec=".", strip.white=TRUE)
A <- c(pls[1],pls[2],pls[3],pls[4])
B <- c(pls[5],pls[6],pls[7],pls[8])

I then put these into the data.fram C, retaining the matrix structure per
guidelines in the JSS article on the pls package

C <- data.frame(h = I(as.matrix(A)), c = I(as.matrix(B)))
showData(C, placement='-20+200', font=getRcmdr('logFont'), maxwidth=80,   
maxheight=30)

63  55 1 0  44  37200 4 0
145  52 1 1  33  69300 4 1
104  32 0 1  68  56900 3 1
109  69 1 1  94  44300 6 1
221  61 0 1  72  79800 6 0
110  40 1 1  48  17600 5 1
194  41 0 0  85  58100 4 0
120  76 1 1  19  76700 3 0
210  61 0 0  41  37600 1 0
243 101 1 1  57  40800 5 1
163  62 0 1  64    400 3 0


So the h. and the c. columns should be matrices that I can regress in plsr
function:

apls <- plsr(h ~ c, data = C)
summary(apls)

But this gives me:   [34] ERROR:    invalid type (list) for variable 'h'

I can get the plsr function to work with scalars for both predictor and
response.   Can anyone tell me where I have gone wrong on the pls input?

apls <- plsr(w ~ h, data = pls)
summary(apls)

Data: 	X dimension: 10000 1 
	Y dimension: 10000 1
Fit method: kernelpls
Number of components considered: 1
TRAINING: % variance explained
   1 comps
X  100.000
w    2.293


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