Computes estimated marginal means (EMMs) of the discount rate k from a
fitted beezdiscounting_tmb model (or a structural
beezdiscounting_choice fit, which shares the log k design).
EMMs are computed on the
log k scale (linear in the fixed-effect coefficients) using the
averaging-matrix reference grid, then back-transformed with exp() so
that k = exp(k_log). Standard errors use the beta_k block of
TMB::sdreport()'s fixed-effect covariance; intervals are Wald on the
log scale and exponentiated. Bayesian fits (beezdiscounting_brms,
beezdiscounting_choice_brms) use the same reference grid with
draws-based summaries: posterior medians and equal-tailed quantile
intervals, no delta method.
Arguments
- fit
A
beezdiscounting_tmb, structuralbeezdiscounting_choice,beezdiscounting_brms, orbeezdiscounting_choice_brmsobject.- factors_in_emm
Character vector of factors to retain in the EMM reference grid. A strict subset marginalizes the omitted factors with equal weights across the full crossing of their levels (emmeans' default
weights = "equal");NULL(default) retains all fitted factors;character(0)marginalizes everything to a single grand-mean cell.- at
Named list specifying factor levels and/or continuous-covariate values for conditional EMMs (one numeric per covariate; multiple values warn and use the first).
aton an omitted factor restricts the level set averaged over.- ci_level
Numeric confidence level for intervals (default 0.95).
- ...
Additional arguments (currently unused).
Value
A tibble with columns level, k, k_log,
std.error, conf.low, conf.high. k_log is the
marginal mean on the log k scale; k = exp(k_log);
std.error is the SE of k_log; the intervals are on the
k (natural) scale.
Examples
# \donttest{
sim <- simulate_dd_ip(
n_subjects = 30, n_conditions = 2, delta_k = c(0, log(3)), seed = 1
)
fit <- fit_dd_tmb(sim, factors = "condition", verbose = 0)
get_dd_param_emms(fit)
#> # A tibble: 2 × 6
#> level k k_log std.error conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 condition=C1 0.0112 -4.49 0.152 0.00834 0.0151
#> 2 condition=C2 0.0276 -3.59 0.154 0.0204 0.0373
# }