The MCMC Procedure
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Overview
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Getting Started
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Syntax
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Details
How PROC MCMC Works Blocking of Parameters Sampling Methods Tuning the Proposal Distribution Conjugate Sampling Initial Values of the Markov Chains Assignments of Parameters Standard Distributions Usage of Multivariate Distributions Specifying a New Distribution Using Density Functions in the Programming Statements Truncation and Censoring Some Useful SAS Functions Matrix Functions in PROC MCMC Create Design Matrix Modeling Joint Likelihood Regenerating Diagnostics Plots Caterpillar Plot Posterior Predictive Distribution Handling of Missing Data Floating Point Errors and Overflows Handling Error Messages Computational Resources Displayed Output ODS Table Names ODS Graphics
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Examples
Simulating Samples From a Known Density Box-Cox Transformation Logistic Regression Model with a Diffuse Prior Logistic Regression Model with Jeffreys’ Prior Poisson Regression Nonlinear Poisson Regression Models Logistic Regression Random-Effects Model Nonlinear Poisson Regression Random-Effects Model Multivariate Normal Random-Effects Model Change Point Models Exponential and Weibull Survival Analysis Time Independent Cox Model Time Dependent Cox Model Piecewise Exponential Frailty Model Normal Regression with Interval Censoring Constrained Analysis Implement a New Sampling Algorithm Using a Transformation to Improve Mixing Gelman-Rubin Diagnostics
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| BEGINCNST/ENDCNST Statement |
- BEGINCNST ;
- ENDCNST ;
The BEGINCNST and ENDCNST statements define a block within which PROC MCMC processes the programming statements only during the setup stage of the simulation. You can use the BEGINCNST and ENDCNST statements to define constants or import data set variables into arrays. Storing data in arrays enables you to work with data that are not identically distributed (see the section Modeling Joint Likelihood) or to implement your own Markov chain sampler (see the section UDS Statement). You can also use the BEGINCNST and ENDCNST statements to assign initial values to the parameters (see the section Assignments of Parameters).
Assign Constants
Whenever you have programming statements that calculate constants that do not need to be evaluated multiple times throughout the simulation, you should put them within the BEGINCNST and ENDCNST statements. Using these statements can reduce redundant processing. For example, you can assign a constant to a symbol or fill in an array with numbers:
array cnst[17];
begincnst;
offset = 17;
do i = 1 to 17;
cnst[i] = i * i;
end;
endcnst;
The MCMC procedure evaluates the programming statements with the BEGINCNST/ENDCNST block once and ignores them in the rest of the simulation.
READ_ARRAY Function
Sometimes you might need to store variables, either from the current input data set or from a different data set, in arrays and use these arrays to specify your model. The READ_ARRAY function is convenient for that purpose.
The following two forms of the READ_ARRAY function are available:
- rc = READ_ARRAY (data_set, array) ;
- rc = READ_ARRAY (data_set, array <,"col_name_1"> <, "col_name_2"> <, ...>) ;
rc returns 0 if the function is able to successfully read the data set.
data_set specifies the name of the data set from which the array data is read. The value specified for data_set must be a character literal or a variable that contains the member name (libname.memname) of the data set to be read from.
array specifies the PROC MCMC array variable into which the data is read. The value specified for array must be a local temporary array variable because the function might need to grow or shrink its size to accommodate the size of the data set.
col_name specifies optional names for the specific columns of the data set that are read. If specified, col_name must be a literal string enclosed in quotation marks. In addition, col_name cannot be a PROC MCMC variable. If column names are not specified, PROC MCMC reads all of the columns in the data set.
When SAS translates between an array and a data set, the array is indexed as [row,column].
The READ_ARRAY function attempts to dynamically resize the array to match the dimensions of the input data set. Therefore, the array must be dynamic; that is, the array must be declared with the /NOSYMBOLS option.
For examples that use the READ_ARRAY function, see Modeling Joint Likelihood, Time Independent Cox Model, and Implement a New Sampling Algorithm.