Extracts coefficients and standard errors from a list of lm/glm
fits (one per imputation, usually built with impute_multiple()) and
pools each coefficient via rubin_pool(), automatically passing the
complete-data residual degrees of freedom so the Barnard-Rubin
small-sample adjustment applies.
Value
A tibble with one row per coefficient: term, estimate, std_error, lcl, ucl, df, t, p_value, fmi, r, rel_eff.
Examples
set.seed(1)
d = data.frame(x = rnorm(40), z = rnorm(40))
d$y = 0.5 * d$x + rnorm(40)
d$x[c(4, 9)] = NA
imps = impute_multiple(d, .cols = x, m = 5, maxit = 3, seed = 2)
fits = lapply(imps, \(dat) lm(y ~ x, dat))
pool_fit(fits)
#> Pooled estimates (Rubin's rules)
#> # A tibble: 2 × 7
#> term estimate std_error lcl ucl p_value fmi
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 0.151 0.130 -0.113 0.415 0.255 0.0565
#> 2 x 0.327 0.153 0.0155 0.638 0.0402 0.0557