latent_positions: Extract latent positions as a tidy data frame

Description

Extracts multiplicative latent factor positions (U and V) from a fitted ame, lame or ame_als model and returns them as a tidy data frame suitable for plotting and analysis. Optionally applies Procrustes alignment for dynamic models and includes posterior standard deviations when posterior samples are available.

Usage

latent_positions(object, ...)
# S3 method for ame
latent_positions(object, align = FALSE, ...)
# S3 method for lame
latent_positions(object, align = TRUE, ...)
# S3 method for ame_als
latent_positions(object, align = FALSE, ...)

Value

A data frame with columns:

actor: Character. Actor name (from rownames of U or V).
dimension: Integer. Latent dimension index (1 to R).
time: Character. Time period label. Dynamic fits use the time labels from the input; static (cross-sectional) fits return "1" for every row so downstream filtering by time behaves the same in both cases.
value: Numeric. The posterior mean latent position.
posterior_sd: Numeric. Posterior standard deviation of the latent position, or NA if posterior samples are not available. To enable, fit the model with posterior_opts = posterior_options(save_UV = TRUE).
type: Character. "U" for sender/row positions, "V" for receiver/column positions. Symmetric models have only "U".

Returns a zero-row data frame with correct column names if R = 0.

Arguments

object: A fitted ame, lame or ame_als model object with R > 0.
...: Additional arguments (currently unused).
align: Logical. For dynamic models (dynamic_uv = TRUE), apply Procrustes alignment across time to remove rotational indeterminacy. Default is FALSE for ame objects and TRUE for lame objects.

Author

Cassy Dorff, Shahryar Minhas, Tosin Salau

Examples

Run this code

# \donttest{
data(YX_nrm)
fit <- ame(YX_nrm$Y, Xdyad = YX_nrm$X, R = 2,
           burn = 5, nscan = 5, odens = 1, verbose = FALSE)
lp <- latent_positions(fit)
head(lp)
# }