boot.student2: Bootstrap Student's t-test for 2 independent samples

In Rfast2: A Collection of Efficient and Extremely Fast R Functions II

Bootstrap Student's t-test for 2 independent samples

Description

Bootstrap Student's t-test for 2 independent samples.

Usage

boot.student2(x, y, B = 999)

Arguments

x	A numerical vector with the data.
y	A numerical vector with the data.
B	The number of bootstrap samples to use.

Details

We bootstrap Student's (Gosset's) t-test statistic and not the Welch t-test statistic. For the latter case see the "boot.ttest2" function in Rfast. The difference is that Gosset's test statistic assumes equaility of the variances, which if violated leads to inlfated type I errors. Bootstrap calibration though takes care of this issue. As for the bootstrap calibration, instead of sampling B times from each sample, we sample \sqrt{B} from each of them and then take all pairs. Each bootstrap sample is independent of each other, hence there is no violation of the theory (Chatzipantsiou et al., 2019).

Value

A vector with the test statistic and the bootstrap p-value.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

Efron Bradley and Robert J. Tibshirani (1993). An introduction to the bootstrap. New York: Chapman & Hall/CRC.

Chatzipantsiou C., Dimitriadis M., Papadakis M. and Tsagris M. (2019). Extremely efficient permutation and bootstrap hypothesis tests using R. To appear in the Journal of Modern Applied Statistical Methods.

https://arxiv.org/ftp/arxiv/papers/1806/1806.10947.pdf

See Also

welch.tests, trim.mean

Examples

x <- rexp(40, 4)
y <- rbeta(50, 2.5, 7.5)
t.test(x, y, var.equal = TRUE)
boot.student2(x, y, 9999)

Rfast2 documentation built on Aug. 8, 2023, 1:11 a.m.