perform mixed linear regression in lmerTest package for getting
observed t values or permutation test

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

s2 function

<dl><dt>data</dt>
<dd>A dataframe specifying the data to be analysed</dd>
<dt>formula</dt>
<dd>A formula in the form like'y~x1+x2+x3+(1|x4) or like'y~x1+x2+x3
+(x3|x4) in lmer function</dd></dl>

Arguments

Estimate t values of mixed linear model — s2

<dl>

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


<dt>formula</dt>
<dd>A formula in the form like'y~x1+x2+x3+(1|x4) or like'y~x1+x2+x3
+(x3|x4) in lmer function</dd>

</dl>

s2: Estimate t values of mixed linear model

Description

Usage

Value

Arguments

Examples