Dear experts, I would like to thank the experts for the fast and fruitful advises concerning my posted problem. Some would like to know where contr.SAS comes from. It is part of the NLME library and it could be found in the SASmixed library. However, I think it's not crucial where it comes from to present my problem. My problem was more of general nature about the impact of contrasts to the p-value of parameter estimates than to this special case of contr.SAS. I'm aware that there is some impact but it was surprising for me that I could choose whether PRO is significant or PRE is significant depending on contrasts. Now I would like to summarize the answers. Vito Muggio recommends using Likelihood Ratio Test instead.
1-pchisq(2*pro.pre$loglik[2]-2*pre$loglik[2], df=1) # impact of pro
[1] 0.1962388
1-pchisq(2*pro.pre$loglik[2]-2*pro$loglik[2], df=1) # impact of pre
[1] 0.1259392 PRO and PRE seems not to contribute in a significant way. Brian Ripley advice that the interpretation of main effects in the presence of interactions depends on the coding except for a few special cases (least squares fitting, balance and true contrasts (e.g. not contr.treatment nor contr.SAS) spring to mind). One extreme view is never to look at the coefficients, only at predictions, and although a counsel of perfection it contains a lot of merit. In addition, he refers to chapter 6 of MASS. John Fox writes that parameter estimates should interpret in conformity with the coding employed. When using 0/1 coding in a model with interactions, you shouldn't think of the coefficients for factor(PRO) and factor(PRE) as "main effects." Frank E. Harrell Jr. also recommend to predict values and pointed me to an example of his contrast.Design, part of the Design library.
contrast(f, list(PRO=1,PRE=0),list(PRO=0, PRE=0))
PRE Contrast S.E. Lower Upper Z Pr(>|z|) 0 0.6576294 0.3020151 0.06569076 1.249568 2.18 0.0294
contrast(f, list(PRO=0,PRE=1),list(PRO=0, PRE=0))
PRO Contrast S.E. Lower Upper Z Pr(>|z|) 0 0.0680866 0.3040376 -0.5278161 0.6639893 0.22 0.8228 Rising PRO by 1 seems to be a significant effect, PRE not. In summary, all experts point me to use something, which is independent of a certain contrast matrix. Personally, I would like to prefer the prediction rather than the LR-test, because I have really to spell out what I want to see. Thank you all, Dominik
-----Original Message----- From: Grathwohl,Dominik,LAUSANNE,NRC/NT Sent: jeudi, 7. novembre 2002 10:37 To: r-help at stat.math.ethz.ch Subject: [R] Preferable contrasts? Dear all, I'm working with Cox-regression, because data could be censored. But in this particular case not. Now I have a simple example: PRO and PRE are (0,1) coded. The response is not normal distributed. We are interested in a model which could describe interaction. But my results are depending strongly in the choose of the contrast option. It is clear that there is some dependence in the contrasts, but in this simple case I could get the vice versa effect. My R output:
options(contrasts = c(unordered = "contr.treatment", ordered =
"contr.poly"))
summary(coxph(Surv(ILOG, alive) ~ factor(PRO)*factor(PRE)))
...
coef exp(coef) se(coef) z p
factor(PRO)1 0.6576 1.930 0.302 2.177 0.029
factor(PRE)1 0.0681 1.070 0.304 0.224 0.820
factor(PRO)1:factor(PRE)1 -0.7703 0.463 0.431 -1.789 0.074
...
options(contrasts = c(unordered = "contr.SAS", ordered =
"contr.poly"))
summary(coxph(Surv(ILOG, alive) ~ factor(PRO)*factor(PRE)))
...
coef exp(coef) se(coef) z p
factor(PRO)0 0.113 1.119 0.304 0.370 0.710
factor(PRE)0 0.702 2.018 0.299 2.350 0.019
factor(PRO)0:factor(PRE)0 -0.770 0.463 0.431 -1.789 0.074
...
What would the experts recommend?
Kind regards,
Dominik
Dominik Grathwohl
Biostatistician
Nestl? Research Center
PO Box 44, CH-1000 Lausanne 26
Phone: + 41 21 785 8034
Fax: + 41 21 785 8556
e-mail: dominik.grathwohl at rdls.nestle.com
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