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 [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.