R code:
library(nlme)
library(car)
lme.mod <- lme(log(co2)~(Days+Topography+Rainfall)^2,random=~Topography|Chamber,
correlation=corARMA(p=1,q=0),na.action=na.exclude,
control = lmeControl(msMaxIter = 200, msMaxEval = 500,sing.tol=1e-20),data=df1)
summary(lme.mod)
Anova(lme.mod, type=3)
SAS code
/* remove DAYS from CLASS */
/* make copy of DAYS to use in REPEATED */
/* correct SUBJECT */
/* heterogeneous variances for TOPOGRAPHY */
data df;
set df;
days_x = days;
run;
PROC MIXED DATA= DF MAXFUNC= 500 MAXITER= 200;
WHERE LOGCO2 NE .;
CLASS CHAMBER TOPOGRAPHY(ref=first) days_x;
MODEL LOGCO2= DAYS TOPOGRAPHY RAINFALL
DAYS * TOPOGRAPHY
DAYS * RAINFALL
TOPOGRAPHY * RAINFALL / SOLUTION RESIDUAL DDFM= BW ;
RANDOM CHAMBER(TOPOGRAPHY) / group=topography;
REPEATED days_x / SUBJECT= CHAMBER(topography) TYPE= AR(1);
RUN;
Your two main problems: (1) In your R code, DAYS is numeric; in your original SAS code, DAYS is factor. (2) You mis-specified SUBJECT in the REPEATED statement.
There are small differences in variance-covariance estimates between R and SAS (the data do not suggest or support such a complicated covariance structure, and many estimates are set to zero in MIXED), but otherwise the results match well.
This is just an answer to your coding question. I do not guarantee that this statistical model is appropriate for this study design.
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