Diagnostic Mode
Stan’s diagnostic mode runs a Stan program with data, initializing parameters either randomly or with user-specified initial values, and then evaluates the log probability and its gradients. The gradients computed by the Stan program are compared to values calculated by finite differences.
Diagnostic mode may be configured with two parameters.
Diagnostic Mode Configuration Table. The diagnostic model configuration parameters, constraints, and default values.
| parameter | description | constraints | default |
|---|---|---|---|
epsilon |
finite difference size | (0, infty) | 1e–6 |
error |
error threshold for matching | (0, infty) | 1e–6 |
If the difference between the Stan program’s gradient value and that calculated by finite difference is higher than the specified threshold, the argument will be flagged.
Diagnostic mode output
Diagnostic mode prints the log posterior density (up to a proportion) calculated by the Stan program for the specified initial values. For each parameter, it prints the gradient at the initial parameter values calculated by Stan’s program and by finite differences over Stan’s program for the log probability.
Unconstrained scale
The output is for the variable values and their gradients are on the unconstrained scale, which means each variable is a vector of size corresponding to the number of unconstrained variables required to define it. For example, an \(N \times N\) correlation matrix, requires \(\binom{N}{2}\) unconstrained parameters. The transformations from constrained to unconstrained parameters are based on the constraints in the parameter declarations and described in the reference manual chapter on transforms.
Includes Jacobian
The log density includes the Jacobian adjustment implied by the constraints declared on variables. The Jacobian adjustment for constrained parameter transforms may be turned off for optimization, but there is as of yet no way to turn it off in diagnostic mode.
Configuration options
The general configuration options for diagnostics are the same as those for MCMC. Initial values may be specified, or they may be drawn at random. Setting the random number generator will only have an effect if a random initialization is specified.
Speed warning and data trimming
Due to the application of finite differences, the computation time grows linearly with the number of parameters. This can be require a very long time, especially in models with latent parameters that grow with the data size. It can be helpful to diagnose a model with smaller data sizes in such cases.