The GENMOD Procedure
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Overview
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Getting Started
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Syntax
PROC GENMOD Statement ASSESS Statement BAYES Statement BY Statement CLASS Statement CONTRAST Statement DEVIANCE Statement EFFECTPLOT Statement ESTIMATE Statement EXACT Statement EXACTOPTIONS Statement FREQ Statement FWDLINK Statement INVLINK Statement LSMEANS Statement LSMESTIMATE Statement MODEL Statement OUTPUT Statement Programming Statements REPEATED Statement SLICE Statement STORE Statement STRATA Statement VARIANCE Statement WEIGHT Statement ZEROMODEL Statement
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Details
Generalized Linear Models Theory Specification of Effects Parameterization Used in PROC GENMOD Type 1 Analysis Type 3 Analysis Confidence Intervals for Parameters F Statistics Lagrange Multiplier Statistics Predicted Values of the Mean Residuals Multinomial Models Zero-Inflated Models Generalized Estimating Equations Assessment of Models Based on Aggregates of Residuals Case Deletion Diagnostic Statistics Bayesian Analysis Exact Logistic and Exact Poisson Regression Missing Values Displayed Output for Classical Analysis Displayed Output for Bayesian Analysis Displayed Output for Exact Analysis ODS Table Names ODS Graphics
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Examples
Logistic Regression Normal Regression, Log Link Gamma Distribution Applied to Life Data Ordinal Model for Multinomial Data GEE for Binary Data with Logit Link Function Log Odds Ratios and the ALR Algorithm Log-Linear Model for Count Data Model Assessment of Multiple Regression Using Aggregates of Residuals Assessment of a Marginal Model for Dependent Data Bayesian Analysis of a Poisson Regression Model Exact Poisson Regression
- References
| ESTIMATE Statement |
- ESTIMATE ’label’ contrast-specification </options> ;
The ESTIMATE statement is similar to a CONTRAST statement, except only one-row 
matrices are permitted.
The elements of the ESTIMATE statement are as follows:
- label
identifies the contrast on the output. A label is required for every contrast specified. Labels can be up to 20 characters and must be enclosed in single quotes.
- contrast-specification
-
identifies the effects and their coefficients from which the
matrix is formed. The contrast-specification can be specified in two different ways. The first method applies to all models except the zero-inflated (ZI) distributions (zero-inflated Poisson and zero-inflated negative binomial), and the syntax is: effect values <...effect values>
The second method of specifying a contrast applies only to ZI models, and the syntax is:
effect values <...effect values> @ZERO effect values <...effect values>
where
- effect
identifies an effect that appears in the MODEL statement. The value INTERCEPT or intercept can be used as an effect when an intercept is included in the model. You do not need to include all effects that are included in the MODEL statement.
- values
are constants that are elements of the
vector associated with the effect.
- options
specifies options for the ESTIMATE statement.
For ZI models, sets of effects values before the @ZERO separator correspond to the regression part of the model with regression parameters 
, and effects values after the @ZERO separator correspond to the zero-inflation part of the model with regression parameters 
. In the case of ZI models, a one-row matrix is created for the regression part of the model, another one-row matrix is created for the zero-inflation part of the model, and separate estimates for the two matrices are computed and displayed.
If you use the default less-than-full-rank GLM CLASS variable parameterization, each row is checked for estimability. If PROC GENMOD finds a contrast to be nonestimable, it displays missing values in corresponding rows in the results. See Searle (1971) for a discussion of estimable functions.
The actual estimates, 
, and 
for ZI models, their approximate standard error, and confidence limits are displayed. A Wald chi-square test that = 0 and 
are also displayed.
The approximate standard error of the estimate is computed as the square root of 
, where 
is the estimated covariance matrix of the parameter estimates. If you specify a GEE model in the REPEATED statement, is the empirical covariance matrix estimate.
If you specify the EXP option, then 
, its standard error, and its confidence limits are also displayed.
The construction of the vector and the checking for estimability for an ESTIMATE statement follow the same rules as listed under the CONTRAST statement.
You can specify the following options in the ESTIMATE statement after a slash (/).
- ALPHA=number
requests that a confidence interval be constructed with confidence level
. The value of number must be between 0 and 1; the default value is 0.05. - DIVISOR=number
-
specifies a value by which to divide all coefficients so that fractional coefficients can be entered as integer numerators. For example, you can use
estimate '1/3(A1+A2) - 2/3A3' a 1 1 -2 / divisor=3;
instead of
estimate '1/3(A1+A2) - 2/3A3' a 0.33333 0.33333 -0.66667;
- E
- EXP
requests that
, its standard error, and its confidence limits be computed. If you specify the EXP option, standard errors are computed using the delta method. Confidence limits are computed by exponentiating the confidence limits for . - SINGULAR=number
- EPSILON=number
tunes the estimability checking as described for the CONTRAST statement.