We provide a solution for performing permutation tests on linear and mixed linear regression models. It allows users to obtain accurate pvalues 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/s10683023097996>). 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 pvalue. To improve the efficiency,a stop criterion (Anscombe (1953) <doi:10.1111/j.25176161.1953.tb00121.x>) is adopted to force permutation to stop if the estimated standard deviation of the value falls below a fraction of the estimated pvalue. By doing so, we avoid the need for massive calculations in exact permutation methods while still generating stable and accurate pvalues.
Package details 


Author  Wentao Zeng [aut, cre, cph] 
Maintainer  Wentao Zeng <wentaozeng@aliyun.com> 
License  GPL3 
Version  0.1.9 
Package repository  View on CRAN 
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