[R] RSM in R, optimize/minimize a response

[simon.heintz]

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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