log_prob and grad_log_prob functions
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.
## 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)
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. |
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.
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.
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.
signature(object = "stanfit")
Compute the gradients
for log_prob as well as the log posterior. The latter is returned as
an attribute.
signature(object = "stanfit")
Get the number of unconstrained parameters.
signature(object = "stanfit")
Convert values of the parameter from unconstrained space (given as a vector) to their constrained space (returned as a named list).
signature(object = "stanfit")
Contrary to
constrained, conert values of the parameters from constrained
to unconstrained space.
The Stan Development Team Stan Modeling Language User's Guide and Reference Manual. http://mc-stan.org.
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.