Missing-data control for gllvmTMB()

Configures how gllvmTMB() treats missing responses and missing predictors. Pass the result to the missing = argument of gllvmTMB().

Usage

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

Arguments

response

How to treat rows whose response value is NA. "drop" (default) is the historical complete-case behaviour: rows with a missing response are removed before the likelihood is built. "include" keeps those rows, builds an observed-response mask (is_y_observed), and contributes nothing to the likelihood for the masked rows – the frequentist observed-data likelihood. For the supported routes, the observed-response likelihood contribution matches the corresponding observed rows, while cell identity is preserved for predict_missing().

predictor

How to treat missing predictors (covariates). "fail" (the default): a missing value in the fixed-effect design matrix is an error, exactly as today. "model" treats a missing predictor declared with mi(x) as a latent variable integrated out by the Laplace approximation, with its covariate model supplied via the impute = argument of gllvmTMB(). The current routes fit one modelled predictor at a time: Gaussian fixed-effect, grouped-intercept, or phylogenetic- intercept covariate models, plus fixed-effect binary, ordered, and unordered discrete predictors.

engine

The estimation engine. The supported value is "laplace" (TMB Laplace approximation). "em" (the Gaussian-only EM special case) and "profile" are reserved names, not yet supported.

Value

A named list with elements response, predictor, and engine.

Details

There is deliberately no estimator argument in miss_control(): estimator choice belongs to gllvmTMB(). The default gllvmTMB() fit uses ML; gllvmTMB(REML = TRUE) is a Gaussian-only pilot and does not yet combine with miss_control(response = "include") or mi() predictor models. There is no MI ("multiple imputation") engine here; multiple imputation is a separate workflow.

Under the default miss_control(response = "drop", predictor = "fail", engine = "laplace") the missing-data layer is an exact no-op: a complete- data fit is byte-identical to a fit built before this layer existed.

See also

gllvmTMB() for the missing = argument; gllvmTMBcontrol() for optimiser / initialisation control.

Examples

miss_control()                          # defaults: drop / fail / laplace
#> $response
#> [1] "drop"
#> 
#> $predictor
#> [1] "fail"
#> 
#> $engine
#> [1] "laplace"
#> 
miss_control(response = "include")      # keep missing-response rows (masked)
#> $response
#> [1] "include"
#> 
#> $predictor
#> [1] "fail"
#> 
#> $engine
#> [1] "laplace"
#>