R help - Jul 2015 - A strange problem using pls package

Dear expeRts,


? ? ?Today i used the pls package to do the PLSR, ?i don' t know if anyone
happen to have the same problem as me, it is so strange, my problem is:
?when i used codes like:
?suppose data has 20 columns
? traindata <- data[ 1:10, 1:10]
?testdata <- data[11:15,1:10]
? pls.fit <- plsr(y~x, ncomp = 5,?data = traindata, method=
"simpls", scale = FALSE, model = TRUE, validation = "CV")
ok, i get some result, the srange thing happens when i redo the plsr, i mean, i
use

?traindata <- data[ 1:10, 1:20]
?testdata <- data[11:15,1:20]
?pls.fit <- plsr(y~x, ncomp = 5,?data = traindata, method=
"simpls", scale = FALSE, model = TRUE, validation = "CV")


I get the same result as the first one!!! ?i explore it a lot, even restart my
computer, but still the same, however i changed the data, the result is the
same!!
I think the pls package must write the result into some files, and ?read it back
~~ but i can't say~~ PLZ help me if you happen to know it





--

PO SU
mail: desolator88 at 163.com 
Majored in Statistics from SJTU

"PO SU" <rhelpmaillist at 163.com> writes:
> ?suppose data has 20 columns
> ? traindata <- data[ 1:10, 1:10]
> ?testdata <- data[11:15,1:10]
> ? pls.fit <- plsr(y~x, ncomp = 5,?data = traindata, method=
"simpls", scale = FALSE, model = TRUE, validation = "CV")
> ok, i get some result, the srange thing happens when i redo the plsr, i
mean, i use
>
> ?traindata <- data[ 1:10, 1:20]
> ?testdata <- data[11:15,1:20]
> ?pls.fit <- plsr(y~x, ncomp = 5,?data = traindata, method=
"simpls", scale = FALSE, model = TRUE, validation = "CV")
>
>
> I get the same result as the first one!!!
The reason is probably that you ask plsr() to use the coloumn of
traindata called "x" as the predictor.  Then it will only use that
coloumn, no matter how many coloumns traindata contains.

The usual way of using plsr() is to have a data.frame with a _matrix_ as
the predictor "coloumn", for instance like this:

mydata <- data.frame(y = some_vector, X = I(some_matrix))
mymodel <- plsr(y ~ X, ..., data = mydata)

If you want to have the predictors as separate vectors, you must name
all of them in the formula (y ~ x1 + x2 + x3 + ...), or you can use the
following shortcut to regress y on all the remaining coloumns:
plsr(y ~ ., ..., data = mydata)

-- 
Regards,
Bj?rn-Helge Mevik