wobble

<code>wobble</code> adds noise from a continuous uniform distribution to the
observations to remove ties.

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

wobble 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>check</dt>
<dd>a logical value indicating whether the samples should be checked
for bindings prior to adding uniform noise or not, defaults to
<code>TRUE</code>.</dd></dl>

Arguments

Add random noise to remove ties — wobble

<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>check</dt>
<dd>a logical value indicating whether the samples should be checked
for bindings prior to adding uniform noise or not, defaults to
<code>TRUE</code>.</dd>

</dl>

wobble: Add random noise to remove ties

Description

Usage

Value

Arguments

Details

References

Examples