Build a Bayesian VAR(1) network (unregularized, Mplus-targeted)

Native, pure-R Bayesian VAR(1) that reproduces Mplus's Bayesian (DSEM/time-series) estimates without needing Mplus. It is the unregularized Bayesian counterpart of fit_graphical_var(): instead of a graphical-lasso / EBIC sparse fit, it estimates a full VAR(1) with a flat prior on the temporal coefficients and an inverse-Wishart prior on the residual precision, then reports the temporal network B and the contemporaneous partial-correlation network derived from the residual covariance. With more than one subject the data are within-person centred and pooled (as in fit_graphical_var()).

Usage

fit_var_bayes(
  data,
  vars,
  id = NULL,
  day = NULL,
  beep = NULL,
  lags = 1L,
  scale = TRUE,
  center_within = TRUE,
  n_iter = 4000L,
  n_burnin = NULL,
  n_chains = 2L,
  thin = 1L,
  seed = NULL,
  min_obs = NULL,
  subject = NULL,
  verbose = FALSE
)

Arguments

data: A data.frame or matrix.
vars: Character vector of variable names (length >= 2).
id: Character. Person-ID column, or NULL for a single series.
day: Character. Day/session column, or NULL.
beep: Character. Beep/measurement column, or NULL.
lags: Integer lag order; only 1 is supported.
scale: Logical. Global standardization of each variable. Default TRUE.
center_within: Logical. Within-person centre when >1 id (removes between-person variance, as in fit_graphical_var()). Default TRUE.
n_iter, n_burnin, n_chains, thin: MCMC controls. Defaults 4000, n_iter/2, 2, 1.
seed: Integer or NULL. Base seed (chain c uses seed + c).
min_obs: Integer or NULL. Keep only subjects with at least this many observations.
subject: Optional vector naming the exact subject(s) to analyse.
verbose: Logical. Progress messages. Default FALSE.

Value

A var_bayes_result object (a cograph group with temporal and contemporaneous netobjects) carrying beta, temporal, kappa, PCC, PDC, posterior draws, and a tidy coefs() table (posterior median, SD, 95% CI, one-tailed p, significance by CI excluding 0).

Examples

# \donttest{
set.seed(1)
y <- matrix(0, 200, 2)
for (t in 2:200) y[t, ] <- c(0.4, 0.3) * y[t - 1, ] + rnorm(2)
d <- data.frame(A = y[, 1], B = y[, 2])
fit <- fit_var_bayes(d, vars = c("A", "B"), n_iter = 500, seed = 1)
print(fit)
#> Bayesian VAR(1) result (unregularized, Mplus-targeted)
#>   Variables:    2 (A, B)
#>   Observations: 199
#>   MCMC: 2 chains x 500 iter, 500 draws | max PSR = 1.010
#>   Temporal 95% CIs excluding 0: 2 / 4
#> 
#>   Temporal [directed]
#>     weights [-0.057, 0.426]  |  +2 / -2 edges
#>           A     B
#>     A  0.43 -0.05
#>     B -0.06  0.26
#> 
#>   Contemporaneous [undirected]
#>     weights [-0.012, -0.012]  |  +0 / -1 edges
#>           A     B
#>     A  0.00 -0.01
#>     B -0.01  0.00
#> 
#>   coefs(x) | matrices(x) | edges(x) | nodes(x) | summary(x)
coefs(fit)
#>   outcome predictor    estimate posterior_sd   ci_lower   ci_upper     p
#> 1       A         A  0.42622237   0.06617353  0.3038259 0.55781645 0.000
#> 2       A         B -0.05725438   0.06822709 -0.1831618 0.08338037 0.194
#> 3       B         A -0.05098612   0.07220854 -0.1865525 0.09368754 0.240
#> 4       B         B  0.26411833   0.06976118  0.1318581 0.40487278 0.000
#>   significant
#> 1        TRUE
#> 2       FALSE
#> 3       FALSE
#> 4        TRUE
# }

Build a Bayesian VAR(1) network (unregularized, Mplus-targeted)

Usage

Arguments

Value

See also

Examples