Dear All, I am trying to fit various spatial covariance model such as spherical, exponential, gaussian, power, linear, linear log and other anisotropic structure using proc mixed. The spatial data has west longitude, latitude, and yield. I converted the spatial coordinates into easting and northing for efficient statistical analysis. Each time I run my SAS script for the entire data set which 12009 observations, I got insufficient memory despite running the script on system with 24 GB of RAM. I suspect the inability of REML algorithm to fit the 12009 x 12009 variance-covariance structure of R matrix of the residual term. Please see my script %spatialcov(var= cov=) proc mixed model &var=/ddfm=residual; repeated/subject=intercept type=sp(&cov)(easting northing); parms (25) (20); run; quit; %mend spatialcov; %spatialcov(var=yield cov=sph) %spatialcov(var=yield cov=exp) %spatialcov(var=yield cov=gau) %spatialcov(var=yield cov=pow) %spatialcov(var=yield cov=lin) %spatialcov(var=yield cov=linl) I have to substract a constant 321000 and 5708000 from easting and northing respectively when I discovered that the values are extremely high to facilitate the computation but all effort in vain. Please could any one advise me on better approach? Does it mean PROC MIXED can not fit spatial model with a very large dataset? Could anyone suggest alternative approach or how to modify my SAS code? Look forward to reading from you.
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