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The GENMOD Procedure |

**REPEATED****SUBJECT=**subject-effect < / options >**;**

**SUBJECT=***subject-effect*-
identifies subjects in the input data set.
The
*subject-effect*can be a single variable, an interaction effect, a nested effect, or a combination. Each distinct value, or level, of the effect identifies a different subject, or cluster. Responses from different subjects are assumed to be statistically independent, and responses within subjects are assumed to be correlated. A*subject-effect*must be specified, and variables used in defining the*subject-effect*must be listed in the CLASS statement. The input data set does not need to be sorted by subject. See the SORTED option.

The*options*control how the model is fit and what output is produced. You can specify the following options after a slash (/). **ALPHAINIT=***numbers*-
specifies initial values for log odds ratio regression parameters
if the LOGOR= option is specified for binary data. If this option
is not specified, an initial value of 0.01 is used for all
the parameters.
**CONVERGE=***number*-
specifies the convergence criterion for GEE parameter estimation.
If the maximum absolute difference between regression
parameter estimates is less than the value of
*number*on two successive iterations, convergence is declared. If the absolute value of a regression parameter estimate is greater than 0.08, then the absolute difference normalized by the regression parameter value is used instead of the absolute difference. The default value of*number*is 0.0001. **CORRW**-
displays the estimated working correlation matrix.
**CORRB**-
displays the estimated regression parameter
correlation matrix.
Both model-based and empirical correlations are displayed.
**COVB**-
displays the estimated regression parameter
covariance matrix.
Both model-based and empirical covariances are displayed.
**ECORRB**-
displays the estimated regression parameter
empirical correlation matrix.
**ECOVB**-
displays the estimated regression parameter
empirical covariance matrix.
**INTERCEPT=***number*-
specifies either an initial or a fixed value of the
intercept regression parameter in the GEE model.
If you specify the NOINT option in the MODEL statement,
then the intercept is fixed at the value of
*number*. **INITIAL=***numbers*-
specifies initial values of the regression parameters estimation,
other than the intercept parameter, for GEE estimation.
If this option is not specified, the estimated
regression parameters assuming independence for
all responses are used for the initial values.
**LOGOR=***log odds ratio structure keyword*-
specifies the regression structure of the log odds ratio
used to model the association of the responses from subjects
for binary data. The response syntax must be of the
single variable type, the distribution must be binomial,
and the data must be binary.
The following table displays the log odds ratio structure
keywords and the corresponding log odds ratio regression structures.
See the "Alternating Logistic Regressions" section for
definitions of the log odds ratio types and examples of specifying
log odds ratio models.
You should specify either the LOGOR= or the TYPE= option,
but not both.
**Table 29.1:**Log Odds Ratio Regression Structures**Keyword****Log Odds Ratio****Regression Structure**EXCH exchangeable FULLCLLUST fully parameterized clusters LOGORVAR( *variable*)indicator variable for specifying block effects NESTK *k*-nestedNEST1 1-nested ZFULL fully specified *z*-matrix specified in ZDATA= data setZREP single cluster specification for replicated *z*-matrix specifiedin ZDATA= data set ZREP(matrix) single cluster specification for replicated *z*-matrix **MAXITER=***number***MAXIT=***number*-
specifies the maximum number of iterations
allowed in the iterative GEE estimation process.
The default number is 50.
**MCORRB**-
displays the estimated regression parameter
model-based correlation matrix.
**MCOVB**-
displays the estimated regression parameter
model-based covariance matrix.
**MODELSE**-
displays an analysis of parameter estimates
table using model-based standard errors.
By default, an "Analysis of Parameter Estimates"
table based on empirical standard errors is displayed.
**RUPDATE=***number*-
specifies the number of iterations between updates of the working
correlation matrix. For example, RUPDATE=5 specifies that the
working correlation is updated once for every five regression parameter
updates. The default value of
*number*is 1; that is, the working correlation is updated every time the regression parameters are updated. **SORTED**-
specifies that the input data are grouped
by subject and sorted within subject.
If this option is not specified, then the
procedure internally sorts by
*subject-effect*and*within subject-effect*, if a*within subject-effect*is specified. **SUBCLUSTER=***variable***SUBCLUST=***variable*-
specifies a variable defining subclusters for the 1-nested
or
*k*-nested log odds ratio association modeling structures. **TYPE | CORR=***correlation-structure keyword*-
specifies the structure of the working correlation matrix
used to model the correlation of the responses from subjects.
The following table displays the correlation structure
keywords and the corresponding correlation structures.
The default working correlation type is the independent (CORR=IND).
See the "Details" section for
definitions of the correlation matrix types.
You should specify LOGOR= or TYPE= but not both.
**Table 29.2:**Correlation Structure Types**Keyword****Correlation Matrix Type**AR | AR(1) autoregressive(1) EXCH | CS exchangeable IND independent MDEP(number) *m*-dependent with*m*=numberUNSTR | UN unstructured USER | FIXED (matrix) fixed, user-specified correlation matrix

For example, you can specify a fixed 4 ×4 correlation matrix with the optionTYPE=USER( 1.0 0.9 0.8 0.6 0.9 1.0 0.9 0.8 0.8 0.9 1.0 0.9 0.6 0.8 0.9 1.0 )

**V6CORR**-
specifies that the `Version 6' method of computing the
normalized Pearson chi-square be used for working correlation
estimation
and for model-based covariance matrix scale factor.
**WITHINSUBJECT | WITHIN=***within subject-effect*-
defines an effect specifying the
order of measurements within subjects.
Each distinct level of the
*within subject-effect*defines a different response from the same subject. If the data are in proper order within each subject, you do not need to specify this option.

If some measurements do not appear in the data for some subjects, this option properly orders the existing measurements and treats the omitted measurements as missing values. If the WITHINSUBJECT= option is not used in this situation, measurements may be improperly ordered and missing values assumed for the last measurements in a cluster.

Variables used in defining the*within subject-effect*must be listed in the CLASS statement. **YPAIR=***variable-list*-
specifies the variables in the ZDATA= data set corresponding
to pairs of responses for log odds ratio association modeling.
**ZDATA=***SAS-data-set*-
specifies a SAS data set containing either the full
*z*-matrix for log odds ratio association modeling or the*z*-matrix for a single complete cluster to be replicated for all clusters. **ZROW=***variable-list*-
specifies the variables in the ZDATA= data set corresponding
to rows of the
*z*-matrix for log odds ratio association modeling.

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