Learn R Programming
rstan (version 2.8.2)

optimizing-methods: optimizing: obtain a point estimate by maximizing the joint posterior

Description

Obtain a point estimate by maximizing the joint posterior from the model defined by class stanmodel. This method is a generic function of the S4 class stanmodel.

Usage

## S3 method for class 'stanmodel':
optimizing(object, data = list(), 
    seed = sample.int(.Machine$integer.max, 1), init = 'random', 
    check_data = TRUE, sample_file = NULL, 
    algorithm = c("LBFGS", "BFGS", "Newton"),
    verbose = FALSE, hessian = FALSE, as_vector = TRUE, 
    draws = 0, constrained = TRUE, ...)

Arguments

object
An object of class stanmodel.
data
A named list or environment providing the data for the model or a character vector for all the names of objects used as data. See the notes in stan.
seed
The seed for random number generation. The default is generated from 1 to the maximum integer supported by Ron the machine. When a seed is specified by a number, as.integer will be applied to it. If as.integer produc
init
One of digit 0, string "0" or "random", a function that returns a list, or a named list of initial parameter values. "0": initialize all to be zero on the unconstrained support; "
check_data
Logical: if TRUE, the data would be preprocessed; otherwise not. If the data is not checked and preprocessed, it is safe to leave it to be the default TRUE. See the notes in stan
sample_file
A character string of file name for specifying where to write samples for all parameters and other saved quantities. If not provided, files are not created. When the folder specified is not writable, tempdir() is used.
algorithm
One of "Newton", "BFGS", and "LBFGS" indicating which optimization algorithm is used. The default is LBFGS.
verbose
TRUE or FALSE: flag indicating whether to print intermediate output from Stan on the console.
hessian
TRUE or FALSE (the default): flag indicating whether to calculate the Hessian (via numeric differentiation of the gradient function in the unconstrained parameter space)
as_vector
TRUE (the default) or FALSE: flag indicating whether a vector is used to for the point estimate found. A list can be used instead by specifying it to be FALSE
draws
a non-negative integer (that defaults to zero) indicating how many times to draw from a multivariate normal distribution whose parameters are the mean vector and the inverse negative Hessian in the unconstrained space
constrained
A logical scalar indicating, if draws > 0, whether the draws should be transformed to the constrained space defined in the parameters block of the Stan program. Defaults to TRUE.
...
Other optional parameters, refer to the manuals for both CmdStan and Stan.
  1. iter(integer), the maximum number of iterations
  2. save_iterations(logical), whether to save the iterations

Value

  • A list with components if the optimization is done successfully:
  • parThe point estimate found. Its form (vector or list) is determined by argument as_vector.
  • valueThe value of the log-posterior (up to an additive constant, the "lp__" in Stan) corresponding to par.
  • hessianThe Hessian matrix if hessian is TRUE
  • theta_tildeThe matrix of parameter draws in the unconstrained space, if draws > 0
  • log_pIf draws > 0, a vector of length draws that contains the value of the log-posterior evaluated at each row of theta_tilde
  • log_gIf draws > 0, a vector of length draws that contains the value of the logarithm of the multivariate normal density evaluated at each row of theta_tilde
  • If the optimization is not finished for reasons such as feeding wrong data, it returns NULL.

See Also

stanmodel

Examples

Run this code
m <- stan_model(model_code = 'parameters {real y;} model {y ~ normal(0,1);}')
f <- optimizing(m, hessian = TRUE)

Run the code above in your browser using DataLab