maximize_log_lh_p: Maximize the log-likelihood or log-posterior as defined by a...

In mcmcsae: Markov Chain Monte Carlo Small Area Estimation

Maximize the log-likelihood or log-posterior as defined by a sampler closure

Description

Maximize the log-likelihood or log-posterior as defined by a sampler closure

Usage

maximize_log_lh_p(
  sampler,
  type = c("llh", "lpost"),
  method = "BFGS",
  control = list(fnscale = -1),
  ...
)

Arguments

sampler	sampler function closure, i.e. the return value of a call to create_sampler.
type	either "llh" (default) or "lpost", for optimization of the log-likelihood, or the log-posterior, respectively.
method	optimization method, passed to optim.
control	control parameters, passed to optim.
...	other parameters passed to optim.

Value

A list of parameter values that, provided the optimization was successful, maximize the (log-)likelihood or (log-)posterior.

Examples

n <- 1000
dat <- data.frame(
  x = rnorm(n),
  f = factor(sample(1:50, n, replace=TRUE))
)
df <- generate_data(
  ~ reg(~x, name="beta", prior=pr_normal(precision=1)) + gen(~x, factor=~f, name="v"),
  sigma.fixed=TRUE, data=dat
)
dat$y <- df$y
sampler <- create_sampler(y ~ x + gen(~x, factor=~f, name="v"), data=dat)
opt <- maximize_log_lh_p(sampler)
str(opt)
plot(df$par$v, opt$par$v); abline(0, 1, col="red")

mcmcsae documentation built on Oct. 11, 2023, 1:06 a.m.