Well, a 3 dimensional spatial analysis isn't really needed, as you have radial symmetry within each plot. Using the information found here , I would try something like this:
proc mixed data=teaksggrowthrate;
class plot ;
model SG = plot / solution;
repeated / subject= intercept type=sp(powa)(height rrd) /r rcorr;
lsmeans plot ;
run;
If you want LSMEANS at various points on the rrd axis, you have to employ a bit of trickery. First, create a variable in your dataset that is exactly equal to rrd. For this example, I will call it rrd_continuous. Then fit the following:
proc mixed data=teaksggrowthrate;
class plot rrd;
model SG = plot rrd plot*rrd/ solution;
repeated rrd / subject= intercept type=sp(powa)(height rrd_continuous) /r rcorr;
lsmeans plot ;
run;
The covariance type in both models is an anisotropic (not the same correlation in both dimensions) power relationship. Other covariance types that might be appropriate would be anisotropic exponential [ SP(POWA)(height rrd_continuous) ] and 2D exponential, geometrically anisotropic [ SP(EXPGA)(height rrd_continuous). You could select amongst these by selecting the model with the smallest AIC or corrected AIC. Short descriptions of these structures are found in the documentation under the REPEATED statement at the TYPE= section.
Also, be aware that the arguments to the spatial covariance structures MUST be continuous, and so should not appear in the CLASS statement, which is why you have to have rrd_continuous in the dataset for the second MIXED example.
SteveDenham
... View more
