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

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

library(lme4) data(sleepstudy) m <- lmer(Reaction ~ Days + (1 | Subject), data = sleepstudy) find_algorithm(m)
#> $algorithm #> [1] "REML" #> #> $optimizer #> [1] "nloptwrap" #>
# NOT RUN { library(rstanarm) m <- stan_lmer(Reaction ~ Days + (1 | Subject), data = sleepstudy) find_algorithm(m) # }