Convert mixed model with repeated measures from SAS to R - r

Question

I have been trying to convert a repeated measures model from SAS to R, since a collaborator will do the analysis but does not have SAS. We are dealing with 4 groups, 8 to 10 animals per group, and then 5 time points for each animal. The mock data file is available here https://drive.google.com/file/d/0B-WfycVUQyhaVGU2MUpuQkg4Mk0/edit?usp=sharing as a Rdata file and here https://drive.google.com/file/d/0B-WfycVUQyhaR0JtZ0V4VjRkTk0/edit?usp=sharing as an excel file:
The original SAS code (1) is :
proc mixed data=essai.data_test method=reml;
class group time mice;
model param = group time group*time / ddfm=kr;
repeated time / type=un subject=mice group=group;
run;
Which gives :
Type 3 Tests des effets fixes
DDL DDL Valeur
Effet Num. Res. F Pr > F
group 3 15.8 1.58 0.2344
time 4 25.2 10.11 <.0001
group*time 12 13.6 1.66 0.1852
I know that R does not handle degrees of freedom in the same way as SAS does, so I am first trying to obtain results similar to (2) :
proc mixed data=essai.data_test method=reml;
class group time mice;
model param = group time group*time;
repeated time / type=un subject=mice group=group;
run;
I have found some hints here Converting Repeated Measures mixed model formula from SAS to R and when specifying a compound symmetry correlation matrix this works perfectly. However, I am not able to obtain the same thing for a general correlation matrix.
With (2) in SAS, I obtain the following results :
Type 3 Tests des effets fixes
DDL DDL Valeur
Effet Num. Res. F Pr > F
group 3 32 1.71 0.1852
time 4 128 11.21 <.0001
group*time 12 128 2.73 0.0026
Using the following R code :
options(contrasts=c('contr.sum','contr.poly'))
mod <- lme(param~group*time, random=list(mice=pdDiag(form=~group-1)),
correlation = corSymm(form=~1|mice),
weights = varIdent(form=~1|group),
na.action = na.exclude, data = data, method = "REML")
anova(mod,type="marginal")
I obtain:
numDF denDF F-value p-value
(Intercept) 1 128 1373.8471 <.0001
group 3 32 1.5571 0.2189
time 4 128 10.0628 <.0001
group:time 12 128 1.6416 0.0880
The degrees of freedom are similar, but not the tests on fixed effects and I don’t know where this comes from. Would anyone have any idea of what I am doing wrong here?

Answer 1

Your R code differs from the SAS code in multiple ways. Some of them are fixable, but I was not able to fix all the aspects to reproduce the SAS analysis.
The R code fits a mixed effects model with a random mice effect, while the SAS code fits a generalized linear model that allows correlation between the residuals, but there are no random effects (because there is no RANDOM statement). In R you would have to use the gls function from the same nlme package.
In the R code all observations within the same group have the same variance, while in the SAS code you have an unstructured covariance matrix, that is each time-point within each group has its own variance. You can achieve the same effect by using weights=varIdent(form=~1|group*time).
In the R code the correlation matrix is the same for every mouse regardless of group. In the SAS code each group has its own correlation matrix. This is the part that I don't know how to reproduce in R.
I have to note that the R model seems to be more meaningful - SAS estimates way too many variances and correlations (which, by the way, you can see meaningfully arranged using the R and RCORR options to the repeated statement).

Answer 2

"In the R code the correlation matrix is the same for every mouse regardless of group. In the SAS code each group has its own correlation matrix. This is the part that I don't know how to reproduce in R." - Try: correlation=corSymm(~1|group*time)