Compute confidence interval/s for the weight mean parameters

estimate_ci_wmean(
  out,
  treat,
  covar,
  wmean_est,
  alpha = 0.05,
  out_levels = order(unique(out)),
  out_form = NULL,
  out_weights = rep(1, length(out_levels)),
  out_model,
  treat_form = "1",
  ci = c("bca", "wald"),
  nboot = 10000
)

Arguments

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.
wmean_est	The point estimates for weighted means
alpha	Confidence intervals have nominal level 1-`alpha`.
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_weights	A vector of `numeric` weights with length equal to the length of `out_levels`.
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_form	The right-hand side of a regression formula for the working model of treatment probability as a function of covariates
ci	A vector of `characters` indicating which confidence intervals should be computed (`"bca"` and/or `"wald"`)
nboot	Number of bootstrap replicates used to compute bootstrap confidence intervals.

Value

List with wald and bca-estimated confidence intervals for the weighted mean parameters.