The “optimizing” Function in R

• Package: rstan

• Purpose: Performs maximum a posteriori (MAP) estimation for a Stan model

• General class: Bayesian inference

• Required argument(s):

• object: A stan model compiled with a function like “stan_model”.

• data: List or environment containing data for the Stan model.

• Notable optional arguments:

• algorithm: The optimization algorithm to use, such as “LBFGS”, “Newton”, or “BFGS”.

• init: Initial values for optimization.

• seed: Random seed for reproducibility.

• Example:

• # Load the rstan library
  library(rstan)
  
  # Define the Stan model code
  model_code <- '
  data {
  int<lower=0> N;
  real y[N];
  }
  parameters {
  real mu;
  real<lower=0> sigma;
  }
  model {
  y ~ normal(mu, sigma);
  }
  '
  
  # Compile the Stan model
  model <- stan_model(model_code = model_code)
  
  # Perform MAP estimation
  N = 100
  fit <- optimizing(model, data = list(N = N, y = rnorm(N)))
  
  # View summary of MAP estimates
  print(fit)

• This example demonstrates how to use the “optimizing” function from the rstan package to perform maximum a posteriori (MAP) estimation for a compiled Stan model. The function requires the result of fitting a Stan model (stanfit object) as input and allows specifying optional arguments like the optimization algorithm, initial values, and random seed for reproducibility.