Convert a fitted lame/amen AME object back into a netify

Takes the posterior-mean (or point-estimate) prediction matrix from a lame::ame() / lame::ame_als() / amen::ame() fit and wraps it as a netify so the predictions can be summarized, plotted, or compared to the observed netlet via compare_networks().

Usage

from_lame_fit(
  fit,
  value = c("fitted", "residual", "prob", "prob_lower", "prob_upper", "fitted_lower",
    "fitted_upper"),
  symmetric = NULL,
  alpha = 0.05
)

Arguments

fit: A fitted object from lame::ame(), lame::ame_als(), lame::lame(), or amen::ame(). Must expose a fitted-value matrix (e.g., fit$EZ, fit$ZpostMean, or fitted(fit)). For value in "prob_lower" / "prob_upper" / "fitted_lower" / "fitted_upper" the fit must additionally expose a per-draw array of fitted values. The slots searched, in order, are: fit$BOOT$EZ and fit$BOOT$Y_hat (lame ALS parametric / block bootstrap), then fit$EZ_draws, fit$EZps, and fit$Z_draws (Gibbs posterior draws), with shape [n, n, B] or [n, n, T, B].
value: One of "fitted" (default — posterior-mean linear predictor EZ/ZpostMean), "residual" (observed - fitted), "prob" (logistic / probit -> probability scale, when the family supports it), or "prob_lower" / "prob_upper" / "fitted_lower" / "fitted_upper" (per-cell alpha/1-alpha quantiles across bootstrap or posterior draws). When lame exposes a fitted() method for the object, that's preferred. For value = "prob" with a binary family, link detection follows this priority: (1) any explicit link slot on the fit (or fit$control$link); (2) ALS class (any token containing "als") or fit$fit_method = "als" -> probit, matching lame::ame_als(); (3) lame class -> logit, matching lame::lame() Gibbs default; (4) ame / amen class (and no fit_method = "logit" override) -> probit, matching amen::ame() Gibbs convention; (5) otherwise logit fallback.
symmetric: Logical. Override the inferred symmetry; default reads from the fit's stored mode/Y.
alpha: Numeric in (0, 0.5). Tail probability for the *_lower / *_upper quantiles. Default 0.05 -> 90% interval (lower = 0.025, upper = 0.975 when interpreted as a two-sided CI; here we use lower = alpha/2, upper = 1 - alpha/2).

Value

A cross-sectional netify object whose underlying matrix is the fitted-value (or residual) matrix at the same actor ordering.

Details

Useful for posterior-predictive checks: build the observed netlet, run an AME fit, then from_lame_fit(fit) |> plot(style = "heatmap") to visualize the fitted intensity matrix.

Author

Cassy Dorff, Shahryar Minhas

Examples

if (FALSE) { # \dontrun{
lm_in <- to_lame(net, fit_method = "als", bootstrap = 200)
fit <- lame::ame_als(Y = lm_in$Y, mode = "bipartite", family = "binary",
bootstrap = 200)
# round-trip predictions back into a netify for plotting
pred_net <- from_lame_fit(fit, value = "prob")
plot(pred_net, style = "heatmap")
} # }