[R] ANOVA Permutation Test

[Juan Telleria Ruiz de Aguirre]

Dear R users,

I have the following Question related to Package lmPerm:

This package uses a modified version of aov() function, which uses
Permutation Tests instead of Normal Theory Tests for fitting an Analysis of
Variance (ANOVA) Model.

However, when I run the following code for a simple linear model:

library(lmPerm)

e$t_Downtime_per_Intervention_Successful %>%
  aovp(
    formula = `Downtime per Intervention[h]` ~ `Working Hours`,
    data = .
  ) %>%
  summary()

I obtain different p-values for each run!

With a regular ANOVA Test, I obtain instead a constant F-statistic, but I
do not fulfill the required Normality Assumptions.

So my questions are:

Would it still be possible use the regular aov() by generating permutations
in advance (Obtaining therefore a Normal Distribution thanks to the Central
Limit Theorem)? And applying the aov() function afterwards? Does it have
sense?


Or maybe this issue could be due to unbalanced classes? I also tried to
weight observations based on proportions, but the function failed.


Any alternative solution for performing a One-Way ANOVA Test over
Non-Normal Data?


Thank you.

Juan

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