rt.test (version 1.18.7.9)

Robustified-t-test: Robustified t-test

Description

Performs robustified one-sample t-test on a vector of data.

Usage

rt.test(x, alternative = c("two.sided", "less", "greater"), 
  mu = 0, test.stat = c("TA", "TB"), conf.level = 0.95)

Arguments

x

vector of quantiles.

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.

mu

a number indicating the true value of the mean.

test.stat

a character string specifying the test statistic.

conf.level

confidence level of the interval.

Value

A list with class "htest" containing the following components:

statistic

the value of the test statistic.

parameter

sample size (non-missing observations in the sample).

p.value

the p-value for the test.

conf.int

a confidence interval for the mean appropriate to the specified alternative hypothesis.

estimate

the specified hypothesized value of the median (TA) or the Hodges-Lehmann (TB).

sample.size

numeric scalar containing the number of non-missing observations in the sample used for the hypothesis test

null.value

the specified hypothesized value of the true mean.

alternative

a character string describing the alternative hypothesis.

method

a character string indicating which statistic (TA or TB) is used.

data.name

a character string giving the name(s) of the data.

Details

Based on the empirical distributions of the TA statistic (based on median and MAD) and the TB statistic (based on Hodges-Lehmann and Shamos), this function performs one-sample robustified t-test.

References

Park, C. and M. Wang (2018). Empirical distributions of the robustified t-test statistics. ArXiv e-prints, 1807.02215. https://arxiv.org/abs/1807.02215

Jeong, R., S. B. Son, H. J. Lee, and H. Kim (2018). On the robustification of the z-test statistic. Presented at KIIE Conference, Gyeongju, Korea. April 6, 2018.

Park, C. (2018). Note on the robustification of the Student t-test statistic using the median and the median absolute deviation. ArXiv e-prints, 1805.12256. https://arxiv.org/abs/1805.12256

See Also

t.test for performing the Student t-test. prop.test for testing the proportion.

Examples

# NOT RUN {
# For robustified t-test (two-sided) using median and MAD (TA).
#    test.stat="TA" (default)
x = rnorm(10) 
rt.test(x)

# For robustified t-test (two-sided) using Hodges-Lehmann and Shamos (TB).
x = rnorm(10)
rt.test(x, test.stat="TB")

# 90% CI (two sides).
x = rnorm(10)
rt.test(x, conf.level=0.9)

# 90% CI (one side).
x = rnorm(10)
rt.test(x, alternative="less", conf.level=0.9)

# }