# Extracting log probabilities and gradients for diagnostics

CmdStan can return the computed log probability and the gradient with respect to a set of parameters.

This is similar to the `diagnose`

subcommand, but the output format differs and the results here are not compared with those from finite differences.

**Note**: Startup and data initialization costs mean that this method is not an efficient way to calculate these quantities. It is provided only for convenience and should not be used for serious computation.

## Configuration

This method takes 3 arguments:

`jacobian`

- Whether or not the Jacobian adjustment for constrained parameters should be included in the gradient. Default value is`true`

(include adjustment).`constrained_params`

- Input file of parameters values on the constrained scale. A single set of constrained parameters can be specified using JSON format. Alternatively, the input file can be set of draws in StanCSV format.`unconstrained_params`

- Input file (JSON or R dump) of parameter values on unconstrained scale. These files should contain a single variable, called`params_r`

, which is a flattened vector of all unconstrained parameters. If this object is two dimensional, each entry should be a vector of the same form and the output will feature multiple rows.

Only one of `constrained_params`

and `unconstrained_params`

can be specified.

For more on the differences between constrained and unconstrained parameters, see the Stan reference manual section on variable transforms.

## CSV output

The output file consists of the following pieces of information:

The full set of configuration options available for the

`log_prob`

method is reported at the beginning of the output file as CSV comments.Column headers, the first column is labelled

`lp__`

, and the rest are named after parameters. These will be the unconstrained parameters, regardless of whether constrained or unconstrained parameters were supplied as input.Values which correspond to the value of the log density (column 1) and the gradient with respect to each parameter (remaining columns).

For example, if we have a file called params.json:

```
{
"theta" : 0.1
}
```

We can run the example model:

`/bernoulli log_prob constrained_params=params.json data file=bernoulli.data.json`

This yields

```
# stan_version_major = 2
# stan_version_minor = 31
# stan_version_patch = 0
# model = bernoulli_model
# start_datetime = 2022-11-17 20:46:06 UTC
# method = log_prob
# log_prob
# unconstrained_params = (Default)
# constrained_params = params.json
# jacobian = true (Default)
# id = 1 (Default)
# data
# file = bernoulli.data.json
# init = 2 (Default)
# random
# seed = 2390820139 (Default)
# output
# file = output.csv (Default)
# diagnostic_file = (Default)
# refresh = 100 (Default)
# sig_figs = -1 (Default)
# profile_file = profile.csv (Default)
# num_threads = 1 (Default)
# stanc_version = stanc3 2.31.0 (Linux)
# stancflags =
lp_,theta
-7.856,1.8
```