Good morning
I'm trying to optimize (minimize actually) a response from a DOE with 4
factors. The 4 factors were built from a Latin Hypercube DOE design type.
Then I proceeded this experiment on 28 different cases, so each case will
include 2000 experiments.
I would like to find the factor quartet that will minimize globally the
response.
How can I find it? I built the script shown below, and I take the eigen
values from the rsm summary, but it's wrong, isn't it ?
I hope it's clear :)
Thank you in advance
Regards
I built the following script:
setwd("C:/Folder")
data <- read.table("File.txt",header=TRUE)
str(data)
summary(data)
data$block <- rep(1:28, each=2000)
library(rsm)
subset <- seq(from=min(data$Case),to=max(data$Case),by=1)
resu <- rsm(Response ~ block + SO(A,B,C,D),data=data[subset,])
summary(resu)
The file looks like the following:
Case A B C D Response
1 1.05243 1.32528 0.974352 1.03963 0.01615749
2 1.10323 1.055 0.937314 1.19282 0.017107937
3 1.12744 1.06457 0.772495 1.44226 0.016988281
4 1.17818 1.07334 1.40521 1.73733 0.016978022
5 1.17297 1.07055 0.910072 1.15935 0.017274737
6 1.14439 0.705105 0.91889 1.78162 0.01699969
7 1.0403 0.778101 1.02743 1.41937 0.017164506
8 1.0847 0.770317 1.16855 1.04109 0.017394582
9 1.03789 1.23609 1.43767 1.52393 0.015932553
10 1.12329 0.68861 1.23011 1.49413 0.01698659
...
11 ...
...
2150 ...
...
55999 1.19111 1.48329 0.880659 1.82682 0.037803564
56000 1.11901 1.12973 0.523026 1.92828 0.038733914
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