win_mean

<code>win_mean</code> calculates the winsorized mean 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

win_mean function

<dl><dt>x</dt>
<dd>a (non-empty) numeric vector of data values.</dd>
<dt>gamma</dt>
<dd>a numeric value in [0, 0.5] specifying the fraction of observations
to be replaced at each end of the sample before calculating the
mean. The default value is 0.2.</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

Winsorized mean — win_mean

<dl>

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


<dt>gamma</dt>
<dd>a numeric value in [0, 0.5] specifying the fraction of observations
to be replaced at each end of the sample before calculating the
mean. The default value is 0.2.</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>

win_mean: Winsorized mean

Description

Usage

Value

Arguments

Examples