Hi, Jim.
Actually you need to have ad "data.frame" object of define X and Y variables
in it.
DATA$X must contain all data for X varibales (DATA$NIR in example)
DATA$Y response variable(s) for your X matrix data: (DATA$density in example)
For example you have X variables in matrix and one Y variables as vector:
> X <- matrix(rnorm(100),10,5)
> Y <- c(1:10)
Now create data.frame (SampN variable isn't essential for plsr model, but it's
easier to form a data frame so):
> plsDATA <- data.frame(SampN=c(1:nrow(X)))
> plsDATA$X <- X
> plsDATA$Y <- as.matrix(Y)
> str (plsDATA)
'data.frame': 10 obs. of 3 variables:
$ SampN: int 1 2 3 4 5 6 7 8 9 10
$ X : num [1:10, 1:5] 1.330 1.025 -1.931 0.552 0.126 ...
$ Y : int 1 2 3 4 5 6 7 8 9 10
> dim (plsDATA)
[1] 10 3
> dim (plsDATA$X)
[1] 10 5
> dim (plsDATA$Y)
[1] 10 1
And fit a model:
> library (pls)
> plsr (X~Y,data=plsDATA)
Partial least squares regression, fitted with the kernel algorithm.
Call:
plsr(X ~ Y, data = plsDATA)
On Tuesday 16 October 2007 21:23:21 Bricklemyer, Ross S wrote:
> Jim,
>
> I tried str(yarn). I received the following output:
>
> 'data.frame': 28 obs. of 3 variables:
> $ NIR : num [1:28, 1:268] 3.07 3.07 3.08 3.08 3.10 ...
> ..- attr(*, "dimnames")=List of 2
> .. ..$ : NULL
> .. ..$ : NULL
> $ density: num 100.0 80.2 79.5 60.8 60.0 ...
> $ train : logi TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
> TRUE ...
>
> I think the important structure for my application is in the NIR line.
> Now that I "know" what the structure is, what does it mean, and how do I
> get my data into the same structure?
>
> Ross
--
Andris Jankevics
Assistant
Department of Medicinal Chemistry
Latvian Institute of Organic Synthesis
Aizkraukles 21, LV-1006, Riga, Latvia
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