Abstract
Many data sets in agricultural research have spatially correlated observations. Examples include field trials conducted on heterogeneous plots for which blocking is inadequate, soil fertility surveys, ground water resource research, etc. Such data sets may be intended for treatment comparisons or for characterization. In either case, linear models with correlated errors are typically used. Geostatistical models such as those used in "kriging" are often used to estimate the error structure .
SAS PROC MIXED allows the estimation of the parameters of mixed linear models with correlated errors. Fixed and random effects are estimated by generalized least squares. Variance and covariance components are estimated by restricted maximum likelihood (REML) .
The purpose of this presentation is to show how PROC MIXED can be used to work with spatial data. Several examples will be presented to illustrate how various analyses could be approached and some of the pitfalls users may encounter .
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Marx, David B. and Stroup, Walter W.
(1993).
"ANALYSIS OF SPATIAL VARIABILITY USING PROC MIXED,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1371
ANALYSIS OF SPATIAL VARIABILITY USING PROC MIXED
Many data sets in agricultural research have spatially correlated observations. Examples include field trials conducted on heterogeneous plots for which blocking is inadequate, soil fertility surveys, ground water resource research, etc. Such data sets may be intended for treatment comparisons or for characterization. In either case, linear models with correlated errors are typically used. Geostatistical models such as those used in "kriging" are often used to estimate the error structure .
SAS PROC MIXED allows the estimation of the parameters of mixed linear models with correlated errors. Fixed and random effects are estimated by generalized least squares. Variance and covariance components are estimated by restricted maximum likelihood (REML) .
The purpose of this presentation is to show how PROC MIXED can be used to work with spatial data. Several examples will be presented to illustrate how various analyses could be approached and some of the pitfalls users may encounter .
