Configure missing-data handling

miss_control() configures the first drmTMB missing-data slices. The default keeps the existing complete-case behaviour. In the current fitted slices, response = "include" is implemented for univariate Gaussian response masks and bivariate Gaussian partial-response rows without dense known covariance. predictor = "model" is implemented mainly for one mi() missing predictor at a time in a univariate Gaussian location model: numeric missing predictors can use Gaussian fixed-effect, grouped, or structured predictor models. Binary, ordered categorical, unordered categorical, strict beta/proportion, zero-one beta boundary proportion, denominator-aware beta-binomial success/trial proportion, Poisson, negative-binomial, or zero-truncated negative-binomial count, positive continuous lognormal or Gamma, and exact-zero semi-continuous Tweedie missing predictors can use fixed-effect predictor models supplied by impute_model(). The non-Gaussian response slices support poisson(), binomial(), nbinom2(), and beta() responses, each with one fixed-effect Bernoulli/logit binary missing predictor. EM/profile engines and simulation-based imputation summaries are reserved for later slices.

Usage

miss_control(
  response = c("drop", "include"),
  predictor = c("fail", "model"),
  engine = c("laplace", "em", "profile")
)

Arguments

response

Response missingness policy. "drop" keeps existing complete-case fitting; "include" keeps rows with supported missing Gaussian responses and masks or marginalizes their likelihood contribution.

predictor

Predictor missingness policy. "fail" errors on missing predictors. "model" enables the current mi() predictor-model routes when paired with a matching impute formula or impute_model() in drmTMB().

engine

Missing-data engine. Only "laplace" is implemented in this slice.

Value

A drm_missing_control object.

Examples

miss_control()
#> $response
#> [1] "drop"
#> 
#> $predictor
#> [1] "fail"
#> 
#> $engine
#> [1] "laplace"
#> 
#> attr(,"class")
#> [1] "drm_missing_control"
miss_control(response = "include")
#> $response
#> [1] "include"
#> 
#> $predictor
#> [1] "fail"
#> 
#> $engine
#> [1] "laplace"
#> 
#> attr(,"class")
#> [1] "drm_missing_control"
miss_control(predictor = "model")
#> $response
#> [1] "drop"
#> 
#> $predictor
#> [1] "model"
#> 
#> $engine
#> [1] "laplace"
#> 
#> attr(,"class")
#> [1] "drm_missing_control"