YuenTTest: Yuen t-Test For Trimmed Means

Description

Performs one and two sample Yuen t-tests for trimmed means on vectors of data.

Usage

YuenTTest(x, ...)
"YuenTTest"(x, y = NULL, alternative = c("two.sided", "less", "greater"),  mu = 0, paired = FALSE, conf.level = 0.95, trim = 0.2, ... )
"YuenTTest"(formula, data, subset, na.action, ...)

Arguments

numeric vector of data values. Non-finite (e.g. infinite or missing) values will be omitted.

an optional numeric vector of data values: as with x non-finite values will be omitted.

alternative

is a character string, one of "greater", "less", or "two.sided", or the initial letter of each, indicating the specification of the alternative hypothesis. For one-sample tests, alternative refers to the true median of the parent population in relation to the hypothesized value of the mean.

paired

a logical indicating whether you want a paired z-test.

a number specifying the hypothesized mean of the population.

conf.level

confidence level for the interval computation.

trim

the fraction (0 to 0.5) of observations to be trimmed from each end of x before the mean is computed. Values of trim outside that range are taken as the nearest endpoint.

formula

a formula of the form lhs ~ rhs where lhs gives the data values and rhs the corresponding groups.

data

an optional matrix or data frame (or similar: see model.frame) containing the variables in the formula formula. By default the variables are taken from environment(formula).

subset

an optional vector specifying a subset of observations to be used.

na.action

a function which indicates what should happen when the data contain NAs. Defaults to getOption("na.action").

...

further arguments to be passed to or from methods.

Value

An object of class htest containing the following components: containing the following components:

References

Wilcox, R. R. (2005) Introduction to robust estimation and hypothesis testing. Academic Press. Yuen, K. K. (1974) The two-sample trimmed t for unequal population variances. Biometrika, 61, 165-170.

Examples

Run this code

x <- rnorm(25, 100, 5)
YuenTTest(x, mu=99)

# the classic interface
with(sleep, YuenTTest(extra[group == 1], extra[group == 2]))

# the formula interface
YuenTTest(extra ~ group, data = sleep)


# Stahel (2002), pp. 186, 196  
d.tyres <- data.frame(A=c(44.5,55,52.5,50.2,45.3,46.1,52.1,50.5,50.6,49.2),
                      B=c(44.9,54.8,55.6,55.2,55.6,47.7,53,49.1,52.3,50.7))
with(d.tyres, YuenTTest(A, B, paired=TRUE))


d.oxen <- data.frame(ext=c(2.7,2.7,1.1,3.0,1.9,3.0,3.8,3.8,0.3,1.9,1.9),
                     int=c(6.5,5.4,8.1,3.5,0.5,3.8,6.8,4.9,9.5,6.2,4.1))
with(d.oxen, YuenTTest(int, ext, paired=FALSE))