lame (version 1.3.4)

lame_parallel: Run LAME (longitudinal AME) with multiple parallel chains

Description

Thin wrapper around ame_parallel that forces fitter = "lame". Use this for longitudinal data; in particular, multi-chain dynamic_beta / dynamic_uv / dynamic_ab fits are reached through here. The combined fit's $BETA is a 3-D array when dynamic_beta is on, and posterior::rhat(as_draws(fit)) gives R-hat across chains for every per-period coefficient.

Usage

lame_parallel(
  Y,
  n_chains = 4,
  cores = n_chains,
  combine_method = c("pool", "list"),
  ...
)

Value

Same as ame_parallel: a combined lame fit (when combine_method = "pool") or a list of fits.

Arguments

Y

Longitudinal network: list of T relational matrices, or a 3-D array [n, n, T].

n_chains

Number of parallel chains (default 4).

cores

CPU cores to use (default n_chains; 1 = sequential).

combine_method

"pool" (default) or "list".

...

Additional arguments forwarded to lame, including dynamic_beta, dynamic_uv, dynamic_ab, family, nscan, burn, odens, etc.

Examples

# \donttest{
data(YX_bin_list)
fit_pll <- lame_parallel(YX_bin_list$Y, Xdyad = YX_bin_list$X,
                         family = "binary", n_chains = 2, cores = 1,
                         nscan = 50, burn = 10, odens = 5,
                         dynamic_beta = "dyad", verbose = FALSE)
dim(fit_pll$BETA)        # [iter * n_chains, p, T]
fit_pll$chain_indicator  # length iter * n_chains
if (requireNamespace("posterior", quietly = TRUE)) {
  posterior::summarise_draws(posterior::as_draws(fit_pll))
}
# }