log_prob-methods {rstan} | R Documentation |

`log_prob`

and `grad_log_prob`

functions

### Description

Using model's `log_prob`

and `grad_log_prob`

take values from the
unconstrained space of model parameters and (by default) return values in
the same space. Sometimes we need to convert the values of parameters from
their support defined in the parameters block (which might be constrained,
and for simplicity, we call it the constrained space) to the unconstrained
space and vice versa. The `constrain_pars`

and `unconstrain_pars`

functions are used for this purpose.

### Usage

```
## S4 method for signature 'stanfit'
log_prob(object, upars, adjust_transform = TRUE, gradient = FALSE)
## S4 method for signature 'stanfit'
grad_log_prob(object, upars, adjust_transform = TRUE)
## S4 method for signature 'stanfit'
get_num_upars(object)
## S4 method for signature 'stanfit'
constrain_pars(object, upars)
## S4 method for signature 'stanfit'
unconstrain_pars(object, pars)
```

### Arguments

`object` |
An object of class |

`pars` |
An list specifying the values for all parameters on the constrained space. |

`upars` |
A numeric vector for specifying the values for all parameters on the unconstrained space. |

`adjust_transform` |
Logical to indicate whether to adjust
the log density since Stan transforms parameters to unconstrained
space if it is in constrained space. Set to |

`gradient` |
Logical to indicate whether gradients are also computed as well as the log density. |

### Details

Stan requires that parameters be defined along with their support.
For example, for a variance parameter, we must define it
on the positive real line. But inside Stan's samplers all parameters
defined on the constrained space are transformed to an unconstrained
space amenable to Hamiltonian Monte Carlo. Because of this, Stan adjusts
the log density function by adding the log absolute value of the
Jacobian determinant. Once a new iteration is drawn, Stan transforms
the parameters back to the original constrained space without
requiring interference from the user. However, when using the log
density function for a model exposed to R, we need to be careful.
For example, if we are interested in finding the mode of parameters
on the constrained space, we then do not need the adjustment.
For this reason, the `log_prob`

and `grad_log_prob`

functions
accept an `adjust_transform`

argument.

### Value

`log_prob`

returns a value (up to an additive constant) the log posterior.
If `gradient`

is `TRUE`

, the gradients are also returned as an
attribute with name `gradient`

.

`grad_log_prob`

returns a vector of the gradients. Additionally, the vector
has an attribute named `log_prob`

being the value the same as `log_prob`

is called for the input parameters.

`get_num_upars`

returns the number of parameters on the unconstrained space.

`constrain_pars`

returns a list and `unconstrain_pars`

returns a vector.

### Methods

- log_prob
`signature(object = "stanfit")`

Compute`lp__`

, the log posterior (up to an additive constant) for the model represented by a`stanfit`

object. Note that, by default,`log_prob`

returns the log posterior in the*unconstrained*space Stan works in internally. set`adjust_transform = FALSE`

to make the values match Stan's output.- grad_log_prob
`signature(object = "stanfit")`

Compute the gradients for`log_prob`

as well as the log posterior. The latter is returned as an attribute.- get_num_upars
`signature(object = "stanfit")`

Get the number of unconstrained parameters.- constrain_pars
`signature(object = "stanfit")`

Convert values of the parameter from unconstrained space (given as a vector) to their constrained space (returned as a named list).- unconstrain_pars
`signature(object = "stanfit")`

Contrary to`constrained`

, conert values of the parameters from constrained to unconstrained space.

### References

The Stan Development Team
*Stan Modeling Language User's Guide and Reference Manual*.
https://mc-stan.org.

### See Also

### Examples

```
## Not run:
# see the examples in the help for stanfit as well
# do a simple optimization problem
opcode <- "
parameters {
real y;
}
model {
target += log(square(y - 5) + 1);
}
"
opfit <- stan(model_code = opcode, chains = 0)
tfun <- function(y) log_prob(opfit, y)
tgrfun <- function(y) grad_log_prob(opfit, y)
or <- optim(1, tfun, tgrfun, method = 'BFGS')
print(or)
# return the gradient as an attribute
tfun2 <- function(y) {
g <- grad_log_prob(opfit, y)
lp <- attr(g, "log_prob")
attr(lp, "gradient") <- g
lp
}
or2 <- nlm(tfun2, 10)
or2
## End(Not run)
```

*rstan*version 2.32.6 Index]