Hello R Community,
I am using the friedman.test() function to test differences in a non-normally
distributed dataset, with a dependent variable that either a
continuous variable or a ratio and has 2+ groups.
I am using the friedman.test instead of a repeated measures ANOVA because
my dataset violated the assumptions for using an ANOVA. I am looking to
compare response means on an emotion-labelling task, between groups (HR,
HC) and emotions (Happy, Sad, Angry, Fearful) where these variables are my
group and block variables, respectively.
When I use the following command:
> friedman.test(Response~Group|Emotion, data=dataset)
I get the following error:
Error in friedman.test.default(c(1L, 1L, 0L, 0L, 0L, 1L, 0L, 2L, 0L, 0L, :
not an unreplicated complete block design
I believe I have set up my dataset correctly.. where Subject ID is repeated for
the four categories of emotion. The variable Error contains the number of
incorrect response corresponding to each emotion.
*Subj Group Emotion Response*94 HR Happy 2
119 HC Happy 0
....
3 HR Sad 4
61 HC Sad 2
64 HC Sad 0
....etc
I think the error c(1L, 1L, 0L, 0L, 0L, 1L, 0L, 2L, 0L, 0L,... ) corresponds
to my Response variable and might not be happy about is the number of 0's that
appear in that variable. However, this is the reason my dataset is not normally
distributed and I cannot use rmANOVA.
Any ideas how to deal with this error? Or whether I should be using a
different statistical test?
Thanks,
Lindsay
--
Lindsay Hanford, BSc, PhD Candidate
McMaster Integrative Neuroscience Discovery & Study | *Department of
Psychology, Neuroscience & Behaviour *
McMaster University *|* lindsay.hanf...@gmail.com
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