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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