boot2pvalue: Compute the p.value from the distribution under H1
In BuyseTest: Generalized Pairwise Comparisons

boot2pvalue

R Documentation

Compute the p.value from the distribution under H1

Description

Compute the p.value associated with the estimated statistic using a bootstrap sample of its distribution under H1.

Usage

boot2pvalue(
  x,
  null,
  estimate = NULL,
  alternative = "two.sided",
  FUN.ci = quantileCI,
  checkSign = TRUE,
  tol = .Machine$double.eps^0.5
)

Arguments

`x`	[numeric vector] a vector of bootstrap estimates of the statistic.
`null`	[numeric] value of the statistic under the null hypothesis.
`estimate`	[numeric] the estimated statistic.
`alternative`	[character] a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".
`FUN.ci`	[function] the function used to compute the confidence interval. Must take `x`, `alternative`, `conf.level` and `sign.estimate` as arguments and only return the relevant limit (either upper or lower) of the confidence interval.
`checkSign`	[logical] should a warning be output if the sign of the estimate differs from the sign of the mean bootstrap value?
`tol`	[numeric] the absolute convergence tolerance.

Details

For test statistic close to 0, this function returns 1.

For positive test statistic, this function search the quantile alpha such that:

quantile(x, probs = alpha)=0 when the argument alternative is set to "greater".
quantile(x, probs = 0.5*alpha)=0 when the argument alternative is set to "two.sided".

If the argument alternative is set to "less", it returns 1.

For negative test statistic, this function search the quantile alpha such that:

quantile(x, probs = 1-alpha=0 when the argument alternative is set to "less".
quantile(x, probs = 1-0.5*alpha=0 when the argument alternative is set to "two.sided".

If the argument alternative is set to "greater", it returns 1.

Examples

set.seed(10)

#### no effect ####
x <- rnorm(1e3) 
boot2pvalue(x, null = 0, estimate = mean(x), alternative = "two.sided")
## expected value of 1
boot2pvalue(x, null = 0, estimate = mean(x), alternative = "greater")
## expected value of 0.5
boot2pvalue(x, null = 0, estimate = mean(x), alternative = "less")
## expected value of 0.5

#### positive effect ####
x <- rnorm(1e3, mean = 1) 
boot2pvalue(x, null = 0, estimate = 1, alternative = "two.sided")
## expected value of 0.32 = 2*pnorm(q = 0, mean = -1) = 2*mean(x<=0)
boot2pvalue(x, null = 0, estimate = 1, alternative = "greater")  
## expected value of 0.16 = pnorm(q = 0, mean = 1) = mean(x<=0)
boot2pvalue(x, null = 0, estimate = 1, alternative = "less")
## expected value of 0.84 = 1-pnorm(q = 0, mean = 1) = mean(x>=0)

#### negative effect ####
x <- rnorm(1e3, mean = -1) 
boot2pvalue(x, null = 0, estimate = -1, alternative = "two.sided") 
## expected value of 0.32 = 2*(1-pnorm(q = 0, mean = -1)) = 2*mean(x>=0)
boot2pvalue(x, null = 0, estimate = -1, alternative = "greater")
## expected value of 0.84 = pnorm(q = 0, mean = -1) = mean(x<=0)
boot2pvalue(x, null = 0, estimate = -1, alternative = "less") # pnorm(q = 0, mean = -1)
## expected value of 0.16 = 1-pnorm(q = 0, mean = -1) = mean(x>=0)

BuyseTest documentation built on March 31, 2023, 6:55 p.m.