Re: [R] multivariate analysis by using lme

From: Hui-Ju Tsai <h-tsai_at_northwestern.edu>
Date: Tue 22 Aug 2006 - 06:21:50 EST


Thanks very much for all your comments and suggestions. First, the data in my previous mail was made up, not real data. I just used it as an example to state out my problem.

I completely agree that one should do some plot diagnosis and univariate models before jumping into a multivariate approach. In our real data, we have several clinical symptoms to define the disease of interest. I have done normality check and univariate analyses for each symptom separately. It is common that some predictor is the same risk factor for several symptoms leading to the disease outcome. Therefore we think that multivariate analysis may be potential application to take into account multiple testing issue, and provide some information for the combination of high-related clinical measures. Except for testing multi-level factor that I had a problem to get 'lme' work, I have got consistent results for both univariate and multivariate approaches. However if the results go to different directions, it will really bother me because it would be hard to explain the outputs in terms of clinical perspective.

Thanks,
Hui-Ju Tsai

-----Original Message-----
From: hadley wickham [mailto:h.wickham@gmail.com] Sent: Monday, August 21, 2006 2:14 PM
To: Spencer Graves
Cc: Hui-Ju Tsai; r-help@stat.math.ethz.ch Subject: Re: Re: [R] multivariate analysis by using lme

> 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:

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)

The contents of this e-mail message and any attachments are private and confidential communications intended solely for the addressee(s) named in this message. If you are not the intended recipient of this message, please 1) immediately notify the sender by reply e-mail and then delete this message and its attachments and 2) do not read, use, distribute disclose or copy this message and/or any attachments.



R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Tue Aug 22 06:27:22 2006

Archive maintained by Robert King, hosted by the discipline of statistics at the University of Newcastle, Australia.
Archive generated by hypermail 2.1.8, at Tue 22 Aug 2006 - 08:22:42 EST.

Mailing list information is available at https://stat.ethz.ch/mailman/listinfo/r-help. Please read the posting guide before posting to the list.