The SPP Procedure
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Overview
- Getting Started
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Syntax
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DetailsTesting for Complete Spatial RandomnessExploring Interpoint InteractionDistance Functions for Multitype Point PatternsBorder Edge Correction for Distance FunctionsConfidence Intervals for Summary StatisticsRipley-Rasson Window EstimatorCovariate Dependence TestsNonparametric Intensity EstimationInhomogeneous Poisson Process Model FittingOutput Data SetsDisplayed OutputODS Table NamesODS Graphics
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Examples
- References
The SPP procedure displays three fit statistics for model selection. For a model that has p parameters, uses n event observations, and produces a maximum log likelihood Log L, these criteria are calculated as in Table 93.7.
Table 93.7: Fit Statistics
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Option |
Description |
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–2 log likelihood |
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Akaike’s information criterion (AIC) |
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Schwarz criterion or Bayesian information criterion (BIC) |
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The AIC and BIC statistics give two different ways of adjusting the –2 Log L statistic for the number of terms in the model and the number of observations used. These statistics can be used when different models for the same data are compared. Lower values of the statistics indicate a more desirable model.