Returns method-level information from a twophase_sensitivity() fit. The
default view contains analysis status, sample-size information, stochastic
settings, multiple-imputation settings, and available IPW/AIPW weight
diagnostics. More focused views can be requested with type.
Usage
diagnostics(x, ...)
# S3 method for class 'twophase_sensitivity'
diagnostics(
x,
type = c("all", "status", "weights", "mi"),
outcome = NULL,
method = NULL,
...
)Arguments
- x
An object.
- ...
Additional arguments passed to methods.
- type
Diagnostic view. One of
"all","status","weights", or"mi".- outcome
Optional character vector selecting one or more outcomes.
- method
Optional character vector selecting one or more methods. Method names are normalized in the same way as in
twophase_sensitivity().
Details
The function reports diagnostics but does not automatically label values as acceptable or unacceptable. Interpretation of weight variability, effective sample size, imputation behavior, and method failures should be based on the study design and scientific context.
Examples
dat <- twophase_example_data(n = 200, seed = 2026)
fit <- twophase_sensitivity(
data = dat,
outcome = "outcome1",
exposure = "exposure",
covariates = c("age", "sex"),
phase2_covariates = c("marker1", "marker2", "marker3"),
phase2 = "phase2",
methods = c("naive", "cca", "ipw"),
seed = 1001
)
diagnostics(fit)
#> outcome method status message N_phase1 N_phase2 phase2_fraction
#> 1 outcome1 Naive ok <NA> 200 126 0.63
#> 2 outcome1 CCA ok <NA> 200 126 0.63
#> 3 outcome1 IPW ok <NA> 200 126 0.63
#> analysis_seed n_imputations mice_maxit jomo_nburn jomo_nbetween
#> 1 1001 NA NA NA NA
#> 2 1001 NA NA NA NA
#> 3 1001 NA NA NA NA
#> mi_logged_events weight_min weight_p99 weight_max weight_cv weight_ess
#> 1 NA NA NA NA NA NA
#> 2 NA NA NA NA NA NA
#> 3 NA 1.293361 2.14367 2.262247 0.1068651 124.5885
#> weight_ess_fraction
#> 1 NA
#> 2 NA
#> 3 0.9887974