The NLMIXED Procedure
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Overview
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Getting Started
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Syntax
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DetailsModeling Assumptions and NotationIntegral ApproximationsBuilt-in Log-Likelihood FunctionsOptimization AlgorithmsFinite-Difference Approximations of DerivativesHessian ScalingActive Set MethodsLine-Search MethodsRestricting the Step LengthComputational ProblemsCovariance MatrixPredictionComputational ResourcesDisplayed OutputODS Table Names
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Examples
- References
MODEL Statement
MODEL dependent-variable ~distribution ;
The MODEL statement is the mechanism for specifying the conditional distribution of the data given the random effects. You must specify a single dependent variable from the input data set, a tilde ~, and then a distribution with its parameters. Valid distributions are as follows.
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normal(m,v) specifies a normal (Gaussian) distribution with mean m and variance v.
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binary(p) specifies a binary (Bernoulli) distribution with probability p.
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binomial(n,p) specifies a binomial distribution with count n and probability p.
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gamma(a,b) specifies a gamma distribution with shape a and scale b.
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negbin(n,p) specifies a negative binomial distribution with count n and probability p.
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general(ll) specifies a general log likelihood function that you construct using SAS programming statements.
The MODEL statement must follow any SAS programming statements you specify for computing parameters of the preceding distributions. See the section Built-in Log-Likelihood Functions for expressions of the built-in conditional log-likelihood functions.