Find sampling algorithm and optimizers

Returns information on the sampling or estimation algorithm as well as optimization functions, or for Bayesian model information on chains, iterations and warmup-samples.

Usage

find_algorithm(x, ...)

Arguments

x

A fitted model.

...

Currently not used.

Value

A list with elements depending on the model.

For frequentist models:

• algorithm, for instance "OLS" or "ML"

• optimizer, name of optimizing function, only applies to specific models (like gam)

For frequentist mixed models:

• algorithm, for instance "REML" or "ML"

• optimizer, name of optimizing function

For Bayesian models:

• algorithm, the algorithm

• chains, number of chains

• iterations, number of iterations per chain

• warmup, number of warmups per chain

Examples

data(sleepstudy, package = "lme4")
m <- lme4::lmer(Reaction ~ Days + (1 | Subject), data = sleepstudy)
find_algorithm(m)
#> $algorithm
#> [1] "REML"
#> 
#> $optimizer
#> [1] "nloptwrap"
#> 
# \donttest{
data(sleepstudy, package = "lme4")
m <- suppressWarnings(rstanarm::stan_lmer(
  Reaction ~ Days + (1 | Subject),
  data = sleepstudy,
  refresh = 0
))
find_algorithm(m)
#> $algorithm
#> [1] "sampling"
#> 
#> $chains
#> [1] 4
#> 
#> $iterations
#> [1] 2000
#> 
#> $warmup
#> [1] 1000
#> 
# }