PROC MIXED can fit a variety of mixed models. One of the most common mixed models is the split-plot design. The split-plot
design involves two experimental factors, `A`

and `B`

. Levels of `A`

are randomly assigned to whole plots (main plots), and levels of `B`

are randomly assigned to split plots (subplots) within each whole plot. The design provides more precise information about
`B`

than about `A`

, and it often arises when `A`

can be applied only to large experimental units. An example is where `A`

represents irrigation levels for large plots of land and `B`

represents different crop varieties planted in each large plot.

Consider the following data from Stroup (1989a), which arise from a balanced split-plot design with the whole plots arranged in a randomized complete-block design. The
variable `A`

is the whole-plot factor, and the variable `B`

is the subplot factor. A traditional analysis of these data involves the construction of the whole-plot error (`A`

*`Block`

) to test `A`

and the pooled residual error (`B`

*`Block`

and `A`

*`B`

*`Block`

) to test `B`

and `A`

*`B`

. To carry out this analysis with PROC GLM, you must use a TEST statement to obtain the correct F test for `A`

.

Performing a mixed model analysis with PROC MIXED eliminates the need for the error term construction. PROC MIXED estimates
variance components for `Block`

, `A`

*`Block`

, and the residual, and it automatically incorporates the correct error terms into test statistics.

The following statements create a DATA set for a split-plot design with four blocks, three whole-plot levels, and two subplot levels:

data sp; input Block A B Y @@; datalines; 1 1 1 56 1 1 2 41 1 2 1 50 1 2 2 36 1 3 1 39 1 3 2 35 2 1 1 30 2 1 2 25 2 2 1 36 2 2 2 28 2 3 1 33 2 3 2 30 3 1 1 32 3 1 2 24 3 2 1 31 3 2 2 27 3 3 1 15 3 3 2 19 4 1 1 30 4 1 2 25 4 2 1 35 4 2 2 30 4 3 1 17 4 3 2 18 ;

The following statements fit the split-plot model assuming random block effects:

proc mixed; class A B Block; model Y = A B A*B; random Block A*Block; run;

The variables `A`

, `B`

, and `Block`

are listed as classification variables in the CLASS statement. The columns of model matrix consist of indicator variables corresponding to the levels of the fixed effects `A`

, `B`

, and `A`

*`B`

listed on the right side of the MODEL statement. The dependent variable `Y`

is listed on the left side of the MODEL statement.

The columns of the model matrix consist of indicator variables corresponding to the levels of the random effects `Block`

and `A`

*`Block`

. The matrix is diagonal and contains the variance components of `Block`

and `A`

*`Block`

. The matrix is also diagonal and contains the residual variance.

The SAS statements produce Output 63.1.1–Output 63.1.8.

The “Model Information” table in Output 63.1.1 lists basic information about the split-plot model. REML is used to estimate the variance components, and the residual variance is profiled from the optimization.

Output 63.1.1: Results for Split-Plot Analysis

The Mixed Procedure

Model Information | |
---|---|

Data Set | WORK.SP |

Dependent Variable | Y |

Covariance Structure | Variance Components |

Estimation Method | REML |

Residual Variance Method | Profile |

Fixed Effects SE Method | Model-Based |

Degrees of Freedom Method | Containment |

The “Class Level Information” table in Output 63.1.2 lists the levels of all variables specified in the CLASS statement. You can check this table to make sure that the data are correct.

Output 63.1.2: Split-Plot Example (*continued*)

Class Level Information | ||
---|---|---|

Class | Levels | Values |

A | 3 | 1 2 3 |

B | 2 | 1 2 |

Block | 4 | 1 2 3 4 |

The “Dimensions” table in Output 63.1.3 lists the magnitudes of various vectors and matrices. The matrix is seen to be , and the matrix is .

Output 63.1.3: Split-Plot Example (*continued*)

Dimensions | |
---|---|

Covariance Parameters | 3 |

Columns in X | 12 |

Columns in Z | 16 |

Subjects | 1 |

Max Obs per Subject | 24 |

The “Number of Observations” table in Output 63.1.4 shows that all observations read from the data set are used in the analysis.

Output 63.1.4: Split-Plot Example (*continued*)

Number of Observations | |
---|---|

Number of Observations Read | 24 |

Number of Observations Used | 24 |

Number of Observations Not Used | 0 |

PROC MIXED estimates the variance components for `Block`

, `A`

*`Block`

, and the residual by REML. The REML estimates are the values that maximize the likelihood of a set of linearly independent
error contrasts, and they provide a correction for the downward bias found in the usual maximum likelihood estimates. The
objective function is –2 times the logarithm of the restricted likelihood, and PROC MIXED minimizes this objective function
to obtain the estimates.

The minimization method is the Newton-Raphson algorithm, which uses the first and second derivatives of the objective function to iteratively find its minimum. The “Iteration History” table in Output 63.1.5 records the steps of that optimization process. For this example, only one iteration is required to obtain the estimates. The Evaluations column reveals that the restricted likelihood is evaluated once for each of the iterations. A criterion of 0 indicates that the Newton-Raphson algorithm has converged.

Output 63.1.5: Split-Plot Analysis (*continued*)

Iteration History | |||
---|---|---|---|

Iteration | Evaluations | -2 Res Log Like | Criterion |

0 | 1 | 139.81461222 | |

1 | 1 | 119.76184570 | 0.00000000 |

Convergence criteria met. |

The REML estimates for the variance components of `Block`

, `A`

*`Block`

, and the residual are 62.40, 15.38, and 9.36, respectively, as listed in the Estimate column of the “Covariance Parameter Estimates” table in Output 63.1.6.

Output 63.1.6: Split-Plot Analysis (*continued*)

Covariance Parameter Estimates | |
---|---|

Cov Parm | Estimate |

Block | 62.3958 |

A*Block | 15.3819 |

Residual | 9.3611 |

The “Fit Statistics” table in Output 63.1.7 lists several pieces of information about the fitted mixed model, including the residual log likelihood. The Akaike (AIC) and Bayesian (BIC) information criteria can be used to compare different models; the ones with smaller values are preferred. The AICC information criteria is a small-sample bias-adjusted form of the Akaike criterion (Hurvich and Tsai, 1989).

Output 63.1.7: Split-Plot Analysis (*continued*)

Fit Statistics | |
---|---|

-2 Res Log Likelihood | 119.8 |

AIC (Smaller is Better) | 125.8 |

AICC (Smaller is Better) | 127.5 |

BIC (Smaller is Better) | 123.9 |

Finally, the fixed effects are tested by using Type 3 estimable functions (Output 63.1.8).

Output 63.1.8: Split-Plot Analysis (*continued*)

Type 3 Tests of Fixed Effects | ||||
---|---|---|---|---|

Effect | Num DF | Den DF | F Value | Pr > F |

A | 2 | 6 | 4.07 | 0.0764 |

B | 1 | 9 | 19.39 | 0.0017 |

A*B | 2 | 9 | 4.02 | 0.0566 |

The tests match the one obtained from the following PROC GLM statements:

proc glm data=sp; class A B Block; model Y = A B A*B Block A*Block; test h=A e=A*Block; run;

You can continue this analysis by producing solutions for the fixed and random effects and then testing various linear combinations of them by using the CONTRAST and ESTIMATE statements. If you use the same CONTRAST and ESTIMATE statements with PROC GLM, the test statistics correspond to the fixed-effects-only model. The test statistics from PROC MIXED incorporate the random effects.

The various “inference space” contrasts given by Stroup (1989a) can be implemented via the ESTIMATE statement. Consider the following examples:

proc mixed data=sp; class A B Block; model Y = A B A*B; random Block A*Block; estimate 'a1 mean narrow' intercept 1 A 1 B .5 .5 A*B .5 .5 | Block .25 .25 .25 .25 A*Block .25 .25 .25 .25 0 0 0 0 0 0 0 0; estimate 'a1 mean intermed' intercept 1 A 1 B .5 .5 A*B .5 .5 | Block .25 .25 .25 .25; estimate 'a1 mean broad' intercept 1 a 1 b .5 .5 A*B .5 .5; run;

These statements result in Output 63.1.9.

Output 63.1.9: Inference Space Results

The Mixed Procedure

Estimates | |||||
---|---|---|---|---|---|

Label | Estimate | Standard Error | DF | t Value | Pr > |t| |

a1 mean narrow | 32.8750 | 1.0817 | 9 | 30.39 | <.0001 |

a1 mean intermed | 32.8750 | 2.2396 | 9 | 14.68 | <.0001 |

a1 mean broad | 32.8750 | 4.5403 | 9 | 7.24 | <.0001 |

Note that all the estimates are equal, but their standard errors increase with the size of the inference space. The narrow
inference space consists of the observed levels of `Block`

and `A`

*`Block`

, and the t-statistic value of 30.39 applies only to these levels. This is the same t statistic computed by PROC GLM, because it computes standard errors from the narrow inference space. The intermediate inference
space consists of the observed levels of `Block`

and the entire population of levels from which `A`

*`Block`

are sampled. The t-statistic value of 14.68 applies to this intermediate space. The broad inference space consists of arbitrary random levels
of both `Block`

and `A`

*`Block`

, and the t-statistic value of 7.24 is appropriate. Note that the larger the inference space, the weaker the conclusion. However, the
broad inference space is usually the one of interest, and even in this space conclusive results are common. The highly significant
p-value for ’a1 mean broad’ is an example. You can also obtain the ’a1 mean broad’ result by specifying `A`

in an LSMEANS statement. For more discussion of the inference space concept, see McLean, Sanders, and Stroup (1991).

The following statements illustrate another feature of the RANDOM statement. Recall that the basic statements for a split-plot design with whole plots arranged in randomized blocks are as follows.

proc mixed; class A B Block; model Y = A B A*B; random Block A*Block; run;

An equivalent way of specifying this model is as follows:

/* equivalent model */ proc mixed data=sp; class A B Block; model Y = A B A*B; random intercept A / subject=Block; run;

In general, if all of the effects in the RANDOM statement can be nested within one effect, you can specify that one effect by using the SUBJECT= option. The subject effect is, in a sense, “factored out” of the random effects. The specification that uses the SUBJECT= effect can result in faster execution times for large problems because PROC MIXED is able to perform the likelihood calculations separately for each subject.