pval_bound: Randomization test for bounded null hypotheses

In li-xinran/RIQITE: Randomization Inference for Quantiles of Individual Treatment Effects

Randomization test for bounded null hypotheses

Description

Obtain the p-value for testing the bounded null hypothesis H0: tau <= c or H0: tau >= c

Usage

pval_bound(
  Z,
  Y,
  c = 0,
  method.list = list(name = "DIM"),
  alternative = "greater",
  Z.perm = NULL,
  nperm = 10^5,
  impute = "control"
)

Arguments

Z	An n dimensional treatment assignment vector.
Y	An n dimensional observed outcome vector.
c	A scalar or vector specifying the bounded null hypothesis.
method.list	A list specifies the choice of the rank sum test statistic. For example, list(name = "DIM") means the difference-in-means statistic, list(name="Wilcoxon") means the Wilcoxon rank sum statistic, and list(name = "Stephenson", s = 10) means the Stephenson rank sum statistic with parameter s=10.
alternative	A character takes value "greater" and "less" indicating the direction of the alternative hypothesis.
Z.perm	A n \times nperm matrix that specifies the permutated assignments for approximating the null distribution of the test statistic.
nperm	A positive integer representing the number of permutations for approximating the randomization distribution of the test statistic
impute	A character specifies the form of the test statistic. "control" means we use the imputed control potential outcomes in the test statistic, and "both" means we use the imputed observed outcome in the test statistic.

Value

The p-value for testing the specified null hypothesis of interest.

li-xinran/RIQITE documentation built on July 1, 2023, 6:58 p.m.