Generic accessor for the tidy coefficient table stored on a
build_mlvar() result. Returns a data.frame with one row per
(outcome, predictor) pair and columns outcome, predictor,
beta, se, t, p, ci_lower, ci_upper, significant.
Usage
coefs(x, ...)
# S3 method for class 'net_mlvar'
coefs(x, ...)
# Default S3 method
coefs(x, ...)Details
Only the within-person (temporal) coefficients are tabulated -
these are the lagged fixed effects that populate fit$temporal.
The between-subjects effects that go into fit$between are handled
via the D (I - Gamma) transformation and are not exposed as a
separate tidy table.
Examples
# A three-variable ESM panel: 20 people x 20 beeps. `tired` is driven by
# `happy` one beep earlier, so the temporal network should recover it.
if (requireNamespace("lme4", quietly = TRUE)) {
set.seed(1)
n_beep <- 20
ar1 <- function(n, phi) as.numeric(stats::filter(stats::rnorm(n), phi,
method = "recursive"))
panel <- do.call(rbind, lapply(seq_len(20), function(i) {
happy <- ar1(n_beep, 0.4)
data.frame(
id = i,
beep = seq_len(n_beep),
happy = happy + stats::rnorm(1),
calm = ar1(n_beep, 0.3) + stats::rnorm(1),
tired = 0.5 * c(0, happy[-n_beep]) + stats::rnorm(n_beep) +
stats::rnorm(1)
)
}))
fit <- build_mlvar(panel, vars = c("happy", "calm", "tired"),
id = "id", beep = "beep")
fit
coefs(fit)
summary(fit)
}
#> network n_nodes n_edges density mean_abs_weight n_positive n_negative
#> 1 temporal 3 6 1 0.11569402 2 4
#> 2 contemporaneous 3 3 1 0.05304465 1 2
#> 3 between 3 3 1 0.15389239 2 1