get_fit_object: Get fitted object from MCMC results

Description

Get fitted object from MCMC results

Usage

get_fit_object(
  APS,
  BPS,
  UVPS,
  YPS,
  BETA,
  VC,
  GOF,
  Xlist,
  actorByYr,
  colActorByYr = NULL,
  start_vals,
  symmetric,
  tryErrorChecks,
  model.name = NULL,
  U = NULL,
  V = NULL,
  dynamic_uv = FALSE,
  dynamic_ab = FALSE,
  bip = FALSE,
  rho_ab = NULL,
  rho_uv = NULL,
  family = NULL,
  odmax = NULL,
  nA = NULL,
  nB = NULL,
  n_time = NULL,
  Y_obs = NULL,
  G = NULL,
  dynamic_beta = FALSE,
  beta_dynamic_mask = NULL,
  beta_dynamic_groups = NULL,
  rho_beta = NULL,
  sigma_beta = NULL,
  RHO_BETA = NULL,
  SIGMA_BETA = NULL,
  dynamic_rho = FALSE,
  RHO = NULL,
  rho_path = NULL
)

Value

Fitted AME object

Arguments

APS: summed additive sender random effects (or matrix for dynamic)
BPS: summed additive receiver random effects (or matrix for dynamic)
UVPS: summed multiplicative random effects
YPS: summed Y posterior predictive values
BETA: Matrix of draws for regression coefficient estimates
VC: Matrix of draws for variance estimates
GOF: Matrix of draws for goodness of fit calculations
Xlist: List based version of design array
actorByYr: List of actors by time point. In bipartite mode this is the per-year list of row actors.
colActorByYr: Bipartite only. List of column actors by time point; defaults to NULL (unipartite).
start_vals: start_vals for future model run
symmetric: logical indicating whether model is symmetric
tryErrorChecks: list with counts of MCMC errors
model.name: Name of the model (optional)
U: Latent sender positions (optional, for dynamic UV)
V: Latent receiver positions (optional, for dynamic UV)
dynamic_uv: logical indicating whether UV effects are dynamic
dynamic_ab: logical indicating whether additive effects are dynamic
bip: logical indicating whether the network is bipartite
rho_ab: temporal correlation parameter for additive effects (optional)
rho_uv: temporal correlation parameter for multiplicative effects (optional)
family: character string specifying the model family (e.g., "binary", "normal", "poisson")
odmax: vector of maximum ranks for ordinal or fixed rank nomination families
nA: number of actors in first mode (for bipartite networks)
nB: number of actors in second mode (for bipartite networks)
n_time: number of time periods (for longitudinal models)
Y_obs: original observed network (stored for residuals computation)
G: bipartite interaction matrix mapping row to column latent spaces
dynamic_beta: logical or scalar; whether the BETA storage is 3-D (dynamic_beta path). Default FALSE.
beta_dynamic_mask: logical vector marking which coefficients are dynamic.
beta_dynamic_groups: character vector of per-coefficient block labels ("intercept", "dyad", "row", "col"); "" for static coefficients.
rho_beta: named numeric vector of per-block AR(1) rho values (one per dynamic block).
sigma_beta: named numeric vector of per-block AR(1) innovation standard deviations.
RHO_BETA: matrix of per-iteration rho_beta draws (rows = MCMC draw, cols = dynamic block).
SIGMA_BETA: matrix of per-iteration sigma_beta draws.
dynamic_rho: logical indicating whether residual dyadic reciprocity varies by period.
RHO: matrix of per-iteration, per-period dyadic reciprocity draws.
rho_path: numeric vector of period-specific dyadic reciprocity values.

Author

Shahryar Minhas