Hi,
I need to calculate either Error or Normalized values based on the following
principle:
Error = Observed value - reference value
Normalized value = Observed Value - Part average
appraiser <- rep(rep(1:3,c(3,3,3)),10)
trail <- rep(rep(1:3,3),10)
part <- rep(1:10,c(9,9,9,9,9, 9,9,9,9,9))
value <- c(rnorm(9, 57.01,0.01), rnorm(9, 57.06,0.02), rnorm(9, 57.10,0.04),
rnorm(9, 57.07,0.03), rnorm(9, 57.12,0.025),
rnorm(9, 57.02,0.011), rnorm(9, 57.03,0.02), rnorm(9, 57.08,
0.013), rnorm(9, 57.01,0.06), rnorm(9, 57.03,0.015))
off <- cbind(appraiser, trail, part, value)
off <- data.frame(off)
off$appraiser <- factor(off$appraiser)
off$trail <- factor(off$trail)
off$part <- factor(off$part)
par(mfrow=c(1,2))
boxplot(off$value ~ off$part)
## when nicely ordre calculation of error is easy
reference <- rep(c(57.01, 57.06, 57.10, 57.07, 57.12, 57.02, 57.03, 57.08,
57.01, 57.03), c(9,9,9,9,9, 9,9,9,9,9))
off$error <- off$value - reference
boxplot(off$error ~off$part)
This is a constructed example.
How do I find mean for the 10 individual parts and make a off$normalized
where I substract the mean for the part in the dataset from the individual
observed values in the data set?
I would also like to find a good method to come from a data.frame/matrix
where part and reference value and even the avaerage find before where
listed, the reason is that the order of the data are not always as
structured:
part reference mean
1 57.01 57.0102
2 57.06 57.0612
....
--
Klaus F. Østergaard,
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