Hi there
So I seem to be struggling with running any of the statistical tests, I believe because my data is not normally distributed (according to shapiro wilkes test) I technically need to run a Kruskall Wallis test(?). Originally I realised I needed to restructure my matrix into a vector, which I have now done, so that I have the 1st column containing the Simpsons index values across all the samples, and then the 2nd (group column) containing labels of the taxanomic phyla these values are associated with.
I am trying to run a statistical significance test to see if there is a significant difference between one of the taxanomic phyla groups and the other phyla groups, in terms of the simpsons Index values.
tmp_vector=as.vector(tmp)
tmp_vector
labels=as.vector(sapply(colnames(tmp), function(i){rep(i, dim(tmp)[1])}))
labels
fungi_vector = cbind(tmp_vector, labels)
fungi_vector
The former is the code I used to generate my vector.
And here is a snippet of the output.
Could do with some help as to figuring out how to run the test I need to as I'm not very experienced with R.
Regards
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written
by
abb525 •
Hi,
Please permit that I first reproduce some data in the same format as yours:
fungi_vector <- data.frame(
tmp_vector = c(rnorm(10, 0.7, 0.1), rnorm(10, 0.2, 0.1)),
labels = c(rep('Ascomycota', 10), rep('Basidiomycota', 10)))
fungi_vector$tmp_vector <- as.character(fungi_vector$tmp_vector)
str(fungi_vector)
'data.frame': 20 obs. of 2 variables:
$ tmp_vector: chr "0.651365595291301" "0.661684503596836" "0.755288193037626" "0.666716503201382" ...
$ labels : chr "Ascomycota" "Ascomycota" "Ascomycota" "Ascomycota" ...
Então / So, the first issue is that your column of numbers is encoded as characters; so, we first need to convert that to numerical values:
fungi_vector$tmp_vector <- as.numeric(fungi_vector$tmp_vector)
To be proper, we should also convert the second column to categorical values:
fungi_vector$labels <- factor(fungi_vector$labels,
levels = c('Ascomycota','Basidiomycota'))
Then, a Kruskal Wallis test can be done like this:
kruskal.test(tmp_vector ~ labels, fungi_vector)
Kruskal-Wallis rank sum test
data: tmp_vector by labels
Kruskal-Wallis chi-squared = 14.286, df = 1, p-value = 0.0001571
For my 'manufactured' data, I already knew that the p-value would be statistically significant due to the fact that I had pre-selected different means via rnorm() (see above).
We can also plot this:
boxplot(tmp_vector ~ labels, fungi_vector)
I will leave the remainder to you.
Kind regards,
Kevin
So would this then compare if there's a statistically significant difference across all of the groups? There are several more phyla groups in the dataset.
The objective was to basically determine if there was a statistically significant difference between Ascomycota and the other phyla in the data.
Although using the code you showed me has definitely worked in giving me a P value when I run the Kruskall Wallis on the whole dataset.
So would this then compare if there's a statistically significant difference across all of the groups? There are several more phyla groups in the dataset.
Yes.
The objective was to basically determine if there was a statistically significant difference between Ascomycota and the other phyla in the data.
In this case, please try Dunn's post-hoc test via FSA::dunnTest() ( see https://www.rdocumentation.org/packages/FSA/versions/0.8.32/topics/dunnTest ). This can be run in the same way as kruskal.test()
If they are logically paired, then no problem using the Wilcoxon, of course.
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