perm_distribution

<code>perm_distribution()</code> calculates the permutation distribution for
several test statistics.

internal

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

perm_distribution function

<dl><dt>x</dt>
<dd>a (non-empty) numeric vector of data values.</dd>
<dt>y</dt>
<dd>a (non-empty) numeric vector of data values.</dd>
<dt>type</dt>
<dd>a character string specifying the desired test statistic. It must
be one of <code>"HL11"</code> (default),
<code>"HL12", "HL21", "HL22", "MED1"</code>, and <code>"MED2"</code>, where
<code>"HL1"</code>, <code>"HL2"</code> and <code>"MED"</code> specify the location
estimator and the numbers <code>1</code> and <code>2</code> the scale
estimator, see the vignette
<code>vignette("robnptests")</code> for more information.</dd>
<dt>randomization</dt>
<dd>a logical value indicating whether the p-value should be
computed from a permutation (<code>FALSE</code>, default) or a
randomization (<code>TRUE</code>) distribution.</dd>
<dt>n.rep</dt>
<dd>an integer value specifying the number of random splits used to
calculate the randomization distribution if <code>method = "randomization"</code>.
The default is <code>n.rep = 10000</code>.</dd></dl>

Arguments

Permutation distribution for robust test statistics — perm_distribution

<dl>

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


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


<dt>type</dt>
<dd>a character string specifying the desired test statistic. It must
be one of <code>"HL11"</code> (default),
<code>"HL12", "HL21", "HL22", "MED1"</code>, and <code>"MED2"</code>, where
<code>"HL1"</code>, <code>"HL2"</code> and <code>"MED"</code> specify the location
estimator and the numbers <code>1</code> and <code>2</code> the scale
estimator, see the vignette
<code>vignette("robnptests")</code> for more information.</dd>


<dt>randomization</dt>
<dd>a logical value indicating whether the p-value should be
computed from a permutation (<code>FALSE</code>, default) or a
randomization (<code>TRUE</code>) distribution.</dd>


<dt>n.rep</dt>
<dd>an integer value specifying the number of random splits used to
calculate the randomization distribution if <code>method = "randomization"</code>.
The default is <code>n.rep = 10000</code>.</dd>

</dl>

perm_distribution: Permutation distribution for robust test statistics

Description

Usage

Value

Arguments

Details