permute_fun

perform permuation on response variable i.e. y,
using the stop criterion suggested by Anscombe

We provide a solution for performing permutation tests on linear and mixed linear regression models. It
allows users to obtain accurate p-values without making distributional assumptions about the data. By
generating a null distribution of the test statistics through repeated permutations of the response variable,
permutation tests provide a powerful alternative to traditional parameter tests (Holt et al. (2023)
<doi:10.1007/s10683-023-09799-6>). In this early version, we focus on the permutation tests over observed
t values of beta coefficients, i.e.original t values generated by parameter tests. After generating a null
distribution of the test statistic through repeated permutations of the response variable, each observed t
values would be compared to the null distribution to generate a p-value. To improve the efficiency,a stop
criterion (Anscombe (1953) <doi:10.1111/j.2517-6161.1953.tb00121.x>) is adopted to force permutation to stop
if the estimated standard deviation of the value falls below a fraction of the estimated p-value. By doing so,
we avoid the need for massive calculations in exact permutation methods while still generating stable and accurate
p-values.

Wentao Zeng

lmerPerm

Perform Permutation Test on General Linear and Mixed Linear
Regression

permute_fun function

<dl><dt>data</dt>
<dd>A data frame specifying the data to be analysed.</dd>
<dt>mle</dt>
<dd>A string that indicated response variable</dd></dl>

Arguments

This function defines the permutation strategry — permute_fun

<dl>

<dt>data</dt>
<dd>A data frame specifying the data to be analysed.</dd>


<dt>mle</dt>
<dd>A string that indicated response variable</dd>

</dl>

permute_fun: This function defines the permutation strategry

Description

Usage

Value

Arguments

Examples