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 every current univariate fitted response route and for bivariate Gaussian partial-response rows without dense known covariance. It uses an observed-response mask: a missing response contributes no direct response likelihood. 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(), beta(), Gamma(link = "log"), lognormal(), beta_binomial(), and student() responses, each with one fixed-effect Bernoulli/logit binary missing predictor. nbinom2() also admits one fixed-effect Gaussian missing predictor (k = 1; no grouped/structured predictor model). 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 = "laplace"
)

Arguments

response

Response missingness policy. "drop" keeps existing complete-case fitting; for a bivariate Gaussian model, a row with either response missing is omitted. "include" keeps rows with supported missing responses and masks their response 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"