est_samp: Estimate Probability of Censoring by Two-Phase Sampling
In txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions

Description Usage Arguments Details Value

Estimate Probability of Censoring by Two-Phase Sampling

1	est_samp(V, C_samp, fit_type = c("sl", "glm"), sl_learners = NULL)

`V`	A `numeric` vector, `matrix`, `data.frame` or similar object giving the observed values of the covariates known to potentially inform the sampling mechanism.
`C_samp`	A `numeric` vector of observed values of the indicator for inclusion in the second-phase sample.
`fit_type`	A `character` indicating whether to perform the fit using GLMs or a Super Learner ensemble model. If use of Super Learner is desired, then the argument `sl_learners` must be provided.
`sl_learners`	An sl3 `Lrnr_sl` object, a Super Learner ensemble or learner instantiated externally using sl3.

Compute estimates of the sampling probability for inclusion in the the second-phase via the two-phase sampling mechanism. These estimates are used for the creation of inverse probability weights.

A numeric vector of the estimated sampling mechanism.

txshift documentation built on Feb. 11, 2022, 1:08 a.m.