The frequentist counterpart of brms's brm_multiple(): fits the
model on every imputed dataset and pools by Rubin's rules with
Barnard-Rubin adjusted degrees of freedom. Accepts a plain list of
data frames or a mice::mids object.
Arguments
- formula, ...
As in
frm().- data
A list of completed data frames, or a
mice::mids.- level
Confidence level for the
$pooled_varcorrinterval.
Value
A frmtmb_multiple object: pooled (the Rubin table for
the fixed effects, its rows named as brms names them, Intercept
and sigma_Intercept), pooled_varcorr (grp/term/type/estimate/
lwr/upr/df/fmi for the random-effect SDs and correlations; NULL
without random effects), and fits (the per-imputation fits).
fixef(), summary(), vcov(), fitted(), conditional_effects()
and the other methods of frm_multiple-methods pool across the
imputations.
Details
Fixed effects are pooled on the link scale, so distributional
coefficients like sigma appear in $pooled on their link (log)
scale, exactly as in vcov(). Random-effect SDs and correlations
are pooled on the scales where a Wald argument is defensible (log
for SDs and GP ranges, Fisher z for correlations, the
confint_varcorr() convention) and back-transformed, giving
$pooled_varcorr. For missing-data mechanisms beyond imputation,
see in-model mi().
hypothesis() pools arbitrary functions of the parameters by the
same rules, and anova.frmtmb_multiple() compares two nested
frm_multiple() fits with the D1, D2 and D3 rules of mice.
frmtmb.sample::frm_sample() samples the fit of every imputation
and combines the draws, as brms's brm_multiple() does.
Examples
set.seed(8)
n <- 80
x <- rnorm(n)
y <- rnorm(n, 1 + 0.5 * x, 1)
x[sample(n, 15)] <- NA
imps <- lapply(1:3, function(i) {
xi <- x
xi[is.na(xi)] <- sample(x[!is.na(x)], sum(is.na(xi)), TRUE)
data.frame(y = y, x = xi)
})
frm_multiple(bf(y ~ x) + gaussian(), data = imps)
#> Pooled over 3 imputations (Rubin's rules):
#>
#> estimate se df t p fmi
#> Intercept 0.9240 0.1330 73.93 6.946 1.244e-09 0.01093
#> x 0.3156 0.1496 21.49 2.110 4.674e-02 0.24080
#> sigma_Intercept 0.1603 0.0803 70.37 1.996 4.980e-02 0.03063