lame (version 1.3.4)

gof: Compute GOF statistics from saved posterior samples

Description

Computes goodness-of-fit statistics after model estimation by generating posterior predictive networks from the saved MCMC samples. This is useful when the model was fitted with gof = FALSE to speed up MCMC sampling, or when you want to evaluate custom GOF statistics without re-running the model.

Usage

gof(fit, Y = NULL, custom_gof = NULL, nsim = 100, verbose = TRUE)

Value

A matrix of GOF statistics with the same format as if gof=TRUE was used during model estimation. First row contains observed statistics, subsequent rows contain posterior predictive statistics.

Arguments

fit

An ame model object that was run with posterior sampling enabled

Y

Original data. For ame objects, an n x n matrix (or nA x nB for bipartite). For lame objects, a list of matrices (one per time period). If NULL, extracted from the fit object.

custom_gof

Optional custom GOF function(s) - same format as for ame()

nsim

Number of posterior predictive simulations to generate (default 100). If NULL, uses all available posterior samples.

verbose

Logical; print progress information

Author

Cassy Dorff, Shahryar Minhas, Tosin Salau

Details

This function requires that the model was estimated with posterior sampling of the parameters needed to generate posterior predictive datasets. Specifically, it needs:

• BETA: regression coefficients

• VC: variance components

• For models with random effects: samples of a, b

• For models with latent factors: U_samples, V_samples

To enable posterior sampling during model estimation, use: posterior_opts = posterior_options(save_UV = TRUE, save_ab = TRUE)

Computing GOF post-hoc has several advantages:

• Faster MCMC sampling (no GOF overhead)

• Can experiment with different GOF statistics without re-running model

• Can control number of posterior predictive simulations independently

See Also

ame, lame, gof_plot

Examples

# \donttest{
# Run model without GOF during fitting
data(YX_nrm)
fit <- ame(YX_nrm$Y, Xdyad = YX_nrm$X, R = 2, gof = FALSE,
           nscan = 100, burn = 10, odens = 1, verbose = FALSE)

# Compute GOF post-hoc
gof_result <- gof(fit)
# }