mi() marks a predictor whose missing values should be handled by the
missing-data predictor model. Most fitted routes support one mi(x) term in
a univariate Gaussian location formula. Numeric missing predictors can use a
matching Gaussian fixed-effect, one random-intercept, or one intercept-only
structured predictor model supplied through impute, for example
impute = list(x = x ~ z) or
impute = list(x = x ~ z + relmat(1 | line, Q = Q)). Family-aware
fixed-effect predictor models supplied with impute_model() cover binary,
ordered categorical, unordered categorical, strict proportion, zero-one
boundary proportion, denominator-aware beta-binomial proportion, count,
positive continuous, and semi-continuous predictors. The non-Gaussian
response routes support one binary mi() predictor modelled by
family = binomial() for family = poisson(), binomial(), nbinom2(),
and beta().
Examples
bf(y ~ z + mi(x), sigma ~ 1)
#> <drm_formula>
#> y ~ z + mi(x)
#> sigma ~ 1