I am using RW 1.2.3. on an IBM PC 300GL.
Using the data bp.dat which accompanies
Helen Brown and Robin Prescott
1999 Applied Mixed Models in Medicine. Statistics in Practice.
John Wiley & Sons, Inc., New York, NY, USA
which is also found at www.med.ed.ac.uk/phs/mixed. The data file was opened
and initialized with
> dat <- read.table("bp.dat")
> names(dat) <-
c("patient","visit","center","treatment","dbp","dbp1","cf","cf1")> attach(dat)
> Patient <- factor(patient)
> Treatment <- factor(treatment)
> Center <- factor(center)
> Visit <- factor(visit)
> dat1 <- data.frame(Patient,Visit,Center,Treatment,dbp,dbp1)
> sapply(dat1,data.class)
Patient Visit Center Treatment dbp dbp1
"factor" "factor" "factor" "factor"
"numeric" "numeric"
After which the following code was run
> library(nlme) # needed for the contrast contr.SAS
>
options(contrasts=c(factor="contr.SAS",ordered="contr.poly"))
> res <- lm(dbp~Treatment+Visit+dbp1)
> anova(res)
> summary(res)
and was repeated leaving out library(nlme) and replacing
>
options(contrasts=c(factor="contr.SAS",ordered="contr.poly"))
with the following contrasts
>
options(contrasts=c(factor="contr.treatment",ordered="contr.poly"))
>
options(contrasts=c(factor="contr.helmert",ordered="contr.poly"))
>
options(contrasts=c(factor="contr.sum",ordered="contr.poly"))
> library(MASS) # needed for the contrast contr.sdif
>
options(contrasts=c(factor="contr.sdif",ordered="contr.poly"))
The results from anova were igual. For example for the factor Treatment had
the same probabilities in every case but
SAS treatment helmert sum sdif
Pr(>F) 3.073e-05 3.073e-05 3.073e-05 3.073e-05 3.073e-05
the probabilities of the different contrasts were
SAS treatment helmert sum sdif
A-B 0.072635 0.072635 0.072635
A-C 8.69e-06 8.69e-06 0.000322
B-C 0.00875 0.637971 0.00875
Why does the contrast contr.sum have distinct results from the other
contrasts? Which ones are confiable?
Pinheiro and Bates 2000:17 state that
Although the individual parameter estimates for the Type factor are
different
between the two fits, the anova resultas are the same. The difference
in the
parameter estimates simply reflects the fact that different contrasts
are being
estimated.
If the process is repeated with lme in place of lm with
> res <- lm(dbp~Treatment+Visit+dbp1,random=~1|Patient)
in place of
> res <- lm(dbp~Treatment+Visit+dbp1)
SAS treatment helmert sum sdif
AIC 7501.212 7501.212 7511.151 7506.182 7501.212
BIC 7546.116 7546.116 7556.055 7551.086 7546.116
logLik -3741.606 -3741.606 -3746.576 -3744.091 -3741.606
Given that AIC and BIC are calculated logLik, it is reasonable that they
differ given the different values of the logLik, but is it reasonable that
the logLik's are different?
SAS treatment helmert sum sdif
A-B 0.2330 0.2330 0.2330
A-C 0.0033 0.0033 0.0168
B-C 0.0850 0.7553 0.0850
Again, why does the contrast contr.sum have distinct results from the other
contrasts? Which ones are confiable?
Thank you very much
Peter B.
--
Peter B. Mandeville mandevip at deimos.tc.uaslp.mx
Jefe del Depto. de Inform?tica y Bioestad?stica rpe1531 at pasteur.fmed.uaslp.mx
Facultad de Medicine Tel: 48 26-23-45 ext. 232
Universidad Aut?noma de San Luis Potos? Fax: 48 28-23-52
Av. V. Carranza 2405
Col. Los Filtros
Apartado Postal 145
San Luis Potos?, S.L.P.
78210 M?xico
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