The LIFEREG Procedure
- Overview
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Getting Started
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Syntax
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DetailsMissing ValuesModel SpecificationComputational MethodSupported DistributionsPredicted ValuesConfidence IntervalsFit StatisticsProbability PlottingINEST= Data SetOUTEST= Data SetXDATA= Data SetComputational ResourcesBayesian AnalysisDisplayed Output for Classical AnalysisDisplayed Output for Bayesian AnalysisODS Table NamesODS Graphics
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ExamplesMotorette FailureComputing Predicted Values for a Tobit ModelOvercoming Convergence Problems by Specifying Initial ValuesAnalysis of Arbitrarily Censored Data with Interaction EffectsProbability Plotting—Right CensoringProbability Plotting—Arbitrary CensoringBayesian Analysis of Clinical Trial DataModel Postfitting Analysis
- References
If specified, the INEST= data set specifies initial estimates for all the parameters in the model. The INEST= data set must
contain the intercept variable (named Intercept) and all independent variables in the MODEL statement.
If BY processing is used, the INEST= data set should also include the BY variables, and there must be at least one observation for each BY group. If there is more than one observation in one BY group, the first observation read is used for that BY group.
If the INEST= data set also contains the _TYPE_ variable, only observations with _TYPE_ value ’PARMS’ are used as starting values. Combining the INEST= data set and the MAXITER= option in the MODEL statement,
partial scoring can be done, such as predicting on a validation data set by using the model built from a training data set.
You can specify starting values for the iterative algorithm in the INEST= data set. This data set overwrites the INITIAL= option in the MODEL statement, which is a little difficult to use for models including multilevel interaction effects. The INEST= data set has the same structure as the OUTEST= data set but is not required to have all the variables or observations that appear in the OUTEST= data set. One simple use of the INEST= option is passing the previous OUTEST= data set directly to the next model as an INEST= data set, assuming that the two models have the same parameterization. See Example 57.3 for an illustration.