Only after doing the best I could with univariate modeling would
I then consider multivariate modeling. And then I'd want to think very
carefully about whether the multivariate model(s) under consideration
seemed consistent with the univariate results -- and what else they
might tell me that I hadn't already gotten from the univariate model.
If you've already done all this, I'm impressed. In the almost 30 years
since I realized I should try univariate models first and work up to
multivariate whenever appropriate, I've not found one application where
the extra effort seemed justified. R has made this much easier, but I'm
still looking for that special application that would actually require
the multivariate tools.
To add to Spencer's comments, I'd strongly recommend you look at your
data before trying to model it. The attached graph, a scatterplot of
res1 vs res2 values conditional on c1 and c2, with point shape given
by inter, reveals many interesting features of your data:
* res1 and res2 values are highly correlated
* inter is constant for a given c1 and c2
* there are between 1 and 3 points for each level of inter - not very
many and I don't think enough to investigate what the effect of inter
is
The plot was created using the following code:
library(ggplot)
s <- read.table("~/Desktop/sample.txt", header=T)
s <- rename(s, c(two="value"))
s$res2 <- NULL
s <- as.data.frame(cast(s, ... ~ res1))
qplot(X0, X1, c1 ~ c2, data=s, shape=factor(inter))
(note that you will need the latest version of ggplot available from
http://had.co.nz/ggplot)
graph.pdf
Description: Adobe PDF document
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