estimate_pmf: Get a treatment-specific estimate of the conditional PMF....
In drord: Doubly-Robust Estimators for Ordinal Outcomes

Get a treatment-specific estimate of the conditional PMF. Essentially this is a wrapper function for fit_trt_spec_reg, which fits the proportion odds model in a given treatment arm.

estimate_pmf(
  out,
  treat,
  covar,
  out_levels,
  out_form = NULL,
  out_model,
  treat_prob_est,
  stratify = FALSE,
  return_models = TRUE,
  ...
)

`out`	A `numeric` vector containing the outcomes. Missing outcomes are allowed.
`treat`	A `numeric` vector containing treatment status. Missing values are not allowed unless the corresponding entry in `out` is also missing. Only values of 0 or 1 are treated as actual treatment levels. Any other value is assumed to encode a value for which the outcome is missing and the corresponding outcome value is ignored.
`covar`	A `data.frame` containing the covariates to include in the working proportional odds model.
`out_levels`	A `numeric` vector containing all ordered levels of the outcome.
`out_form`	The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT.
`out_model`	Which R function should be used to fit the proportional odds model. Options are `"polr"` (from the `MASS` package), "vglm" (from the `VGAM` package), or `"clm"` (from the `ordinal` package).
`treat_prob_est`	Estimated probability of treatments, output from call to `estimate_treat_prob`.
`stratify`	Boolean indicating whether to use nonparametric maximum likelihood (i.e., a stratified estimator). If `out_form = "1"`, then a covariate-unadjusted estimate is computed.
`return_models`	If `TRUE` the fitted working proportional odds models and treatment probability models are returned.
`...`	Other options (not used).

A list with fm the fitted model for treatment 1 and 0 (or, if !return_models then NULL) and pmf the estimated PMF under treatment 1 and 0 evaluated on each observation.

drord documentation built on May 21, 2021, 1:06 a.m.