estimate_Ypred: Estimator Ypred

In trangnguyen74/tnMediation: Simple and more robust estimation of marginal natural (in)direct effects

Estimator Ypred

Description

Function that implements estimator Ypred

Usage

estimate_Ypred(
  data,
  s.wt.var = NULL,
  cross.world = "10",
  effect.scale = "MD",
  boot.num = 999,
  boot.seed = NULL,
  boot.method = "cont-wt",
  boot.stratify = TRUE,
  a.cm.form,
  max.stabilized.wt = 30,
  plot = TRUE,
  cm.std = NULL,
  cm.order = NULL,
  y.c.form = NULL,
  y.c1.form = NULL,
  y.c0.form = NULL,
  y10.c.form = NULL,
  y01.c.form = NULL,
  y.link = "identity"
)

Arguments

data	A data frame.
s.wt.var	Optional, name of variable containing sampling weights.
cross.world	The cross-world condition involved in the effect decomposition of choice. Should be "10" if want the (NDE0, NIE1) pair, "01" if want the (NIE0, NDE1) pair, or "both" if want both decompositions.
effect.scale	The scale of effect of choice. Defaults to "MD" (i.e., mean/risk difference or additive). If outcome is non-negative, also allows "mean ratio" (which could also be specified as "ratio", "MR", "risk ratio", "rate ratio", "RR"). If outcome is binary or bounded within the (0,1) interval, also allows "odds ratio" (which could also be specified as "OR").
boot.num	Number of bootstrap samples used for interval estimation, defaults to 999. If just want point estimate, set to 0.
boot.seed	Optional, specify bootstrap seed for reproducibility.
boot.method	Method for drawing bootstrap samples. Options: "cont-wt" for continuous weights bootstrap, and "resample" for bootstrap by simple resampling (i.e., integer weights bootstrap).
boot.stratify	Whether bootstrap samples are drawn stratified by treatment variable. Defaults to TRUE.
a.cm.form	Formula for the P(A|C,M) model.
max.stabilized.wt	Max stabilized weight allowed. Larger weights are truncated to this level. Default is 30.
plot	Whether to output weight distribution and balance plots. Defaults to TRUE.
cm.std	blah
cm.order	blah
y.c.form	blah
y.c1.form	blah
y.c0.form	blah
y10.c.form	Model formula for E[Y(1,M0)|C]
y01.c.form	Model formula for E[Y(0,M1)|C]
y.link	blah

See Also

[estimate_YpredMR()] which is closely related but more robust

Other estimators: estimate_NDEpredR(), estimate_NDEpred(), estimate_Y2predR(), estimate_Y2pred(), estimate_YpredMR(), estimate_psYpredMR(), estimate_psYpred(), estimate_wpCadj(), estimate_wpMRCadj(), estimate_wtCadj(), estimate_wtd()

trangnguyen74/tnMediation documentation built on May 3, 2023, 6:58 a.m.