Random-Effects Model

This example illustrates how you can fit a normal likelihood random-effects model in PROC MCMC. PROC MCMC offers you the ability to model beyond the normal likelihood (see Logistic Regression Random-Effects Model, Nonlinear Poisson Regression Random-Effects Model, and Piecewise Exponential Frailty Model).

Consider a scenario in which data are collected in groups and you want to model group-specific effects. You can use a random-effects model (sometimes also known as a variance-components model): where is the group index and indexes the observations in the th group. In the regression model, the fixed effects and are the intercept and the coefficient for variable , respectively. The random effect is the mean for the th group, and are the error term.

Consider the following SAS data set:

```title 'Random-Effects Model';

data heights;
input Family G\$ Height @@;
datalines;
1 F 67   1 F 66   1 F 64   1 M 71   1 M 72   2 F 63
2 F 63   2 F 67   2 M 69   2 M 68   2 M 70   3 F 63
3 M 64   4 F 67   4 F 66   4 M 67   4 M 67   4 M 69
;
```

The response variable Height measures the heights (in inches) of 18 individuals. The covariate is the gender (variable G), and the individuals are grouped according to Family (group index). Since the variable G is a character variable and PROC MCMC does not support a CLASS statement, you need to create the corresponding design matrix. In this example, the design matrix for a factor variable of level 2 (M and F) can be constructed using the following statement:

```data input;
set heights;
if g eq 'F' then gf = 1;
else gf = 0;
drop g;
run;
```

The data set variable gf is a numeric variable and can be used in the regression model in PROC MCMC.

In data sets with factor variables that have more levels, you can consider using PROC TRANSREG to construct the design matrix. See the section Create Design Matrix for more information.

To model the data, you can assume that Height is normally distributed: The priors on the parameters , , are also assumed to be normal:         Priors on the variance terms, and , are inverse-gamma:      The inverse-gamma distribution is a conjugate prior for the variance in the normal likelihood and the variance in the prior distribution of the random effect.

The following statements fit a linear random-effects model to the data and produce the output shown in Figure 54.10 and Figure 54.11:

```ods graphics on;
proc mcmc data=input outpost=postout nmc=50000 thin=5 seed=7893;
ods select Parameters REparameters PostSummaries PostIntervals
parms b0 0 b1 0 s2 1 s2g 1;

prior b: ~ normal(0, var = 10000);
prior s: ~ igamma(0.01, scale = 0.01);
random gamma ~ normal(0, var = s2g) subject=family monitor=(gamma);
mu = b0 + b1 * gf + gamma;
model height ~ normal(mu, var = s2);
run;
ods graphics off;
```

Some of the statements are very similar to those shown in the previous two examples. The ODS GRAPHICS ON statement enables ODS Graphics. The PROC MCMC statement specifies the input and output data sets, the simulation size, the thinning rate, and a random number seed. The ODS SELECT statement displays the model parameter and random-effects parameter information tables, summary statistics table, the interval statistics table, and the diagnostics plots.

The PARMS statement lumps all four model parameters in a single block. They are b0 (overall intercept), b1 (main effect for gf), s2 (variance of the likelihood function), and s2g (variance of the random effect). If a random walk Metropolis sampler is the only applicable sampler for all parameters, then these four parameters are updated in a single block. However, since the conjugate updater is used to draw posterior samples of s2 and s2g, PROC MCMC updates these parameters separately (see the Block column in "Parameters" table in Figure 54.9).

The PRIOR statements specify priors for all the parameters. The notation b: is a shorthand for all symbols that start with the letter ‘b’. In this example, b: includes b0 and b1. Similarly, s: stands for both s2 and s2g. This shorthand notation can save you some typing, and it keeps your statements tidy.

The RANDOM statement specifies a single random effect to be gamma, and specifies that it has a normal prior centered at 0 with variance s2g. The SUBJECT= argument in the RANDOM statement defines a group index (family) in the model, where all observations from the same family should have the same group indicator value. The MONITOR= option outputs analysis for all the random-effects parameters.

Finally, the mu assignment statement calculates the expected value of height in the random-effects model. The MODEL statement specifies the likelihood function for height.

The "Parameters" and "Random-Effects Parameters" tables, shown in Figure 54.9, contain information about the model parameters and the four random-effects parameters.

Figure 54.9 Model and Random-Effects Parameter Information
 Random-Effects Model

The MCMC Procedure

Parameters
Block Parameter Sampling
Method
Initial
Value
Prior Distribution
1 s2 Conjugate 1.0000 igamma(0.01, scale = 0.01)
2 s2g Conjugate 1.0000 igamma(0.01, scale = 0.01)
3 b0 N-Metropolis 0 normal(0, var = 10000)
b1   0 normal(0, var = 10000)

Random Effects Parameters
Parameter Subject Levels Prior Distribution
gamma Family 4 normal(0, var = s2g)

The posterior summary and interval statistics for b0 and b1 are shown in Figure 54.10.

Figure 54.10 Posterior Summary and Interval Statistics
 Random-Effects Model

The MCMC Procedure

Posterior Summaries
Parameter N Mean Standard
Deviation
Percentiles
25% 50% 75%
b0 10000 68.4591 1.2085 67.7939 68.4369 69.0863
b1 10000 -3.5381 0.9622 -4.1439 -3.5351 -2.9466
s2 10000 4.1495 1.9089 2.8251 3.7353 4.9854
s2g 10000 4.8368 17.4110 0.2218 1.2534 4.0669
gamma_1 10000 0.9374 1.2817 0.0832 0.6802 1.6250
gamma_2 10000 0.0167 1.1399 -0.4145 0.0325 0.5038
gamma_3 10000 -1.3313 1.6080 -2.1514 -0.9247 -0.1434
gamma_4 10000 0.0979 1.1495 -0.3470 0.0537 0.5802

Posterior Intervals
Parameter Alpha Equal-Tail Interval HPD Interval
b0 0.050 66.0454 71.1125 65.8787 70.7985
b1 0.050 -5.4303 -1.5336 -5.4941 -1.6259
s2 0.050 1.7532 9.0102 1.4424 8.0066
s2g 0.050 0.0117 29.7402 0.00121 18.3336
gamma_1 0.050 -1.1857 3.8472 -1.1522 3.8666
gamma_2 0.050 -2.6485 2.4385 -2.3792 2.6240
gamma_3 0.050 -5.4020 0.6187 -4.8669 0.8624
gamma_4 0.050 -2.5324 2.6004 -2.2341 2.7602

Trace plots, autocorrelation plots, and posterior density plots for all the parameters are shown in Figure 54.11. The mixing looks very reasonable, suggesting convergence.

Figure 54.11 Plots for and Log of the Posterior Density        From the interval statistics table, you see that both the equal-tail and HPD intervals for are positive, strongly indicating the positive effect of the parameter. On the other hand, both intervals for cover the value zero, indicating that gf does not have a strong impact on predicting height in this model.