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Extract model coefficients from AME model

Source: R/coef.ame.R
coef.ame.Rd

Returns posterior means of regression coefficients from a fitted AME or LAME model.

Usage

# S3 method for class 'ame'
coef(object, ...)

# S3 method for class 'lame'
coef(object, ...)

Arguments

object

Fitted AME model (class "ame" or "lame").

...

Additional arguments (ignored).

Value

Named numeric vector (static fit) or p x T matrix (dynamic_beta fit) of posterior mean coefficients.

Details

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.

See also

vcov.ame for the posterior covariance matrix, confint.ame for credible intervals, summary.ame for a full summary table