doubleGrubbsTest {PMCMRplus} | R Documentation |
Grubbs Double Outlier Test
Description
Performs Grubbs double outlier test.
Usage
doubleGrubbsTest(x, alternative = c("two.sided", "greater", "less"), m = 10000)
Arguments
x |
a numeric vector of data. |
alternative |
the alternative hypothesis.
Defaults to |
m |
number of Monte-Carlo replicates. |
Details
Let denote an identically and independently distributed continuous
variate with realizations
.
Further, let the increasingly ordered realizations
denote
. Then
the following model for testing two maximum outliers can be proposed:
with . The null hypothesis,
H
is tested against the alternative,
H
.
For testing two minimum outliers, the model can be proposed as
The null hypothesis is tested against the alternative,
H.
The p-value is computed with the function pdgrubbs
.
Value
A list with class "htest"
containing the following components:
- method
a character string indicating what type of test was performed.
- data.name
a character string giving the name(s) of the data.
- statistic
the estimated quantile of the test statistic.
- p.value
the p-value for the test.
- parameter
the parameters of the test statistic, if any.
- alternative
a character string describing the alternative hypothesis.
- estimates
the estimates, if any.
- null.value
the estimate under the null hypothesis, if any.
References
Grubbs, F. E. (1950) Sample criteria for testing outlying observations. Ann. Math. Stat. 21, 27–58.
Wilrich, P.-T. (2011) Critical values of Mandel's h and k, Grubbs and the Cochran test statistic. Adv. Stat. Anal.. doi:10.1007/s10182-011-0185-y.
Examples
data(Pentosan)
dat <- subset(Pentosan, subset = (material == "A"))
labMeans <- tapply(dat$value, dat$lab, mean)
doubleGrubbsTest(x = labMeans, alternative = "less")