I have got a data of times series(14 continuos variable and 1 categorical variable). I find out to use a Friedman test. but I know that It just has used in Rank and/or Likert data. I am not sure how to include interaction effects between variables. Therefore, I had a clue which could be possible multiply and/or divide for indepent variables so that, it will able to test interaction effects between indepent variables.
For now, I used a friedman.test() and posthoc.friedman.conover.tes() of PMCMR on R-package to test ANOVA test and Post-hoc test. Another hand, I will use AovSum() of FactormineR on R-package or gam() it is Generalized Additive Models of mgcv on R-package.
PD. I tried to use a many R-package however, It doesn´t work with my data.
Pseudocode:
data.aov=ANOVA(depent variable ~ indepent variable 1*indepent variable 2
*indepent variable 3*indepent variable 4*...indepent variable [n-1], data)
summary(data.aov)
A few resources:
1] https://www.datanovia.com/en/lessons/friedman-test-in-r/
2] https://rcompanion.org/handbook/F_10.html
José Augusto Solís Benites ,Do you have a repeated measure or ordinal/categorized time data? How many IVs(15?), and type of DV. Interaction between what? To me, the tests you are using is inappropriate, misspecification exist, in your model(data.aov) 1. The Friedman test is the non-parametric alternative to the one-way ANOVA with repeated measures. It is used to test for differences between groups when the dependent variable being measured is ordinal. 2. The Jonckheere-Terpstra test is a rank-based nonparametric test that can be used to determine if there is a statistically significant trend between an ordinal independent variable and a continuous or ordinal dependent variable.(if the peruvian fish characteristics can be put in ordered scale.) Except we see the data, know the IV and DV structure, you are the one that knows best what to do, any advice here is off-mark or serious error(meaniless) given your unknown research questions and dataset structure.
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