Performs Yuen's robust t-test for trimmed means. Compared with the classical t-test, the procedure is substantially less sensitive to outliers, heavy tails, and moderate departures from normality.
The test is based on:
trimmed means,
winsorized variances,
Welch-type degrees of freedom.
For paired tests, trimming is performed on the paired differences, following Yuen (1974).
Usage
yuenTTest(x, ...)
# S3 method for class 'formula'
yuenTTest(formula, data, subset, na.action = na.pass, ...)
# Default S3 method
yuenTTest(
x,
y = NULL,
alternative = c("two.sided", "less", "greater"),
mu = 0,
paired = FALSE,
conf.level = 0.95,
trim = 0.2,
...
)Arguments
- x
numeric vector of observations.
- ...
further arguments passed to methods.
- formula
a formula of the form
lhs ~ rhs.- data
optional data frame for the formula interface.
- subset
optional subset expression.
- na.action
NA handling function.
- y
optional second numeric vector.
- alternative
character string specifying the alternative hypothesis. One of
"two.sided","less", or"greater".- mu
hypothesized trimmed mean (or trimmed mean difference).
- paired
logical indicating whether a paired test is performed.
- conf.level
confidence level for the confidence interval.
- trim
fraction of observations trimmed from each tail. Must satisfy
0 <= trim < 0.5.
Details
Robust one-, two-, and paired-sample t-tests based on trimmed means and winsorized variances.
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.
See also
Other test.location:
brunnerMunzelTest(),
hotellingsT2Test(),
moodMedianTest(),
signTest(),
tTestA(),
vanWaerdenTest(),
zTest()
Examples
x <- rnorm(25, 100, 5)
yuenTTest(x, mu = 99)
#>
#> Yuen One-Sample Trimmed Mean t-test
#>
#> data: x
#> t = 0.74889, df = 14.0, trim = 0.2, p-value = 0.4663
#> alternative hypothesis: true trimmed mean difference is not equal to 99
#> 95 percent confidence interval:
#> -2.115739 4.385920
#> sample estimates:
#> trimmed mean of x
#> 100.1351
#>
with(sleep,
yuenTTest(extra[group == 1],
extra[group == 2]))
#>
#> Yuen Two-Sample Trimmed Mean t-test
#>
#> data: extra[group == 1] and extra[group == 2]
#> t = -1.5314, df = 8.7502, trim = 0.2000, p-value = 0.161
#> alternative hypothesis: true trimmed mean difference is not equal to 0
#> 95 percent confidence interval:
#> -1.939437 3.006103
#> sample estimates:
#> trimmed mean of x trimmed mean of y
#> 0.5333333 2.2000000
#>
yuenTTest(extra ~ group, data = sleep)
#>
#> Yuen Two-Sample Trimmed Mean t-test
#>
#> data:
#> t = -1.5314, df = 8.7502, trim = 0.2000, p-value = 0.161
#> alternative hypothesis: true trimmed mean difference is not equal to 0
#> 95 percent confidence interval:
#> -1.939437 3.006103
#> sample estimates:
#> trimmed mean of x trimmed mean of y
#> 0.5333333 2.2000000
#>