Dear Juan
I do not use the package but if it does permutation tests it presumably
uses random numbers and since you are not setting the seed you would get
different values for each run.
Michael
On 03/09/2018 16:17, Juan Telleria Ruiz de Aguirre wrote:
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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--
Michael
http://www.dewey.myzen.co.uk/home.html
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