Returns posterior means of regression coefficients from a fitted AME or LAME model.
# S3 method for ame
coef(object, ...)# S3 method for lame
coef(object, ...)
Named numeric vector (static fit) or p x T matrix
(dynamic_beta fit) of posterior mean coefficients.
Fitted AME model (class "ame" or "lame").
Additional arguments (ignored).
For a static fit (the default, and any model with dynamic_beta = FALSE),
coefficients are returned as a named numeric vector computed as
colMeans(fit$BETA).
For a dynamic fit (lame(..., dynamic_beta = ...) where some
coefficient is time-varying), fit$BETA is a 3-dimensional array
[n_stored, p, T] and coef.lame returns a [p, T]
matrix of per-period posterior means. Rownames are the coefficient names;
colnames are the period labels (from names(Y) or t1, t2, ...).
Static coefficients in a dynamic fit are constant across the columns.
For binary models, these are on the probit (latent) scale. Use
predict.ame with type = "response" to get predicted
probabilities.
What coef() does not return. The multiplicative latent
positions \(U\), \(V\) are not part of the coefficient vector;
they live on fit$U and fit$V (or as 3-D arrays
[n, R, T] when dynamic_uv is on). The additive
sender / receiver effects \(a, b\) are on fit$APM and
fit$BPM. For a tidy frame of latent positions use
latent_positions; for sender / receiver lollipops use
ab_plot.
vcov.ame for the posterior covariance matrix,
confint.ame for credible intervals,
summary.ame for a full summary table