hodges_lehmann

<code>hodges_lehmann</code> calculates the one-sample Hodges-Lehmann estimator
of a sample.

Implementations of several robust nonparametric two-sample tests
for location or scale differences. The test statistics are based on robust
location and scale estimators, e.g. the sample median or the Hodges-Lehmann estimators
as described in Fried & Dehling (2011) <doi:10.1007/s10260-011-0164-1>.
The p-values can be computed via the permutation principle, the randomization principle, or by using
the asymptotic distributions of the test statistics under the null hypothesis, which ensures
(approximate) distribution independence of the test decision. To test for a difference in
scale, we apply the tests for location difference to transformed observations; see Fried (2012) <doi:10.1016/j.csda.2011.02.012>.
Random noise on a small range can be added to the original observations in order to
hold the significance level on data from discrete distributions.
The location tests assume homoscedasticity and the scale tests require the
location parameters to be zero.

Sermad Abbas

robnptests

Robust Nonparametric Two-Sample Tests for Location/Scale

Barbara Brune

Roland Fried

hodges_lehmann function

<dl><dt>x</dt>
<dd>a (non-empty) numeric vector of data values.</dd>
<dt>na.rm</dt>
<dd>a logical value indicating whether NA values in <code>x</code> and
<code>y</code> should be stripped before the computation proceeds. The
default is <code>na.rm = FALSE</code>.</dd></dl>

Arguments

One-sample Hodges-Lehmann estimator — hodges_lehmann

<dl>

<dt>x</dt>
<dd>a (non-empty) numeric vector of data values.</dd>


<dt>na.rm</dt>
<dd>a logical value indicating whether NA values in <code>x</code> and
<code>y</code> should be stripped before the computation proceeds. The
default is <code>na.rm = FALSE</code>.</dd>

</dl>

hodges_lehmann: One-sample Hodges-Lehmann estimator

Description

Usage

Value

Arguments

Details

References

Examples