Hi Klaus,
I am not exactly sure what you are asking for, but something like this? This
would be option (2) from your list - I don't know that it would be too
difficult in R that you would want to use another tool.
filt <- function(x)
with(x,which(Hole > 1))
normalize <- function(x,y)
{
x$Feature <- x$Feature - with(y,Nominel[match(x$Hole,Hole)])
return(x)
}
# from CSV import
mydata <- read.csv(...)
# answer
normalizedData <- normalize(filt(mydata))
--- Klaus Friis Østergaard <[EMAIL PROTECTED]> wrote:
> Hi,
>
> I have a data set which is like this I write as the CSV I import:
>
> Sample;Hole;Feature;Value
> 1;5;x;4,2334
> 1;5;y;3,3434
> 1;5;r;0,1080
> 1;10;x;5,2526
> 1;10;y;4,3434
> 1;10;r;0,1080
> ....
>
> with 98 sample and 10 different holes. These are measured values.
>
> Now I also have a list of nominel values:
>
> Hole;Feature;Nominel;LSL;USL
> 5;x;4,25,4,20;4,30
> 5;y;3,35;3,30;3,40
> 5;r;0,10;0,07;0,13
> 10;x;5,25;5,20;5,30
> ...
>
> If I want to "normalize" the measured values. The results are to be used in
> a Sweave file that I plan to reuse for several similar data sets in the
> furture.
>
> I have been looking at different approache:
> 1. do it in a script out side R
> 2. do it the long and hard way by filtering out all the single holes and
> Feature, the on this list subtract the nominel values, and then combine it
> back.
> 3. ?
>
> Any help and guidence apriciated
> --
> Klaus F. Østergaard, <farremosen(at)gmail dot com>
>
> [[alternative HTML version deleted]]
>
> > ______________________________________________
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> and provide commented, minimal, self-contained, reproducible code.
>
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