Here is some sample code:
## Simulation function to create data, analyze it using
## kruskal.test, and return the p-value
## change rexp to change the simulation distribution
simfun <- function(means, k=length(means), n=rep(50,k)) {
mydata <- lapply( seq_len(k), function(i) {
rexp(n[i], 1)
Thank you very much Greg, I will give that a try.
Best,
Collin.
On Fri, Apr 3, 2015 at 1:43 PM, Greg Snow <538...@gmail.com> wrote:
> Here is some sample code:
>
> ## Simulation function to create data, analyze it using
> ## kruskal.test, and return the p-value
> ## change rexp to change
Thank you Jim, I did see those (though not my typo :) and am still
pondering the warning about post-hoc analyses.
The situation that I am in is that I have a set of individuals who
have been assigned a course grade. We have then clustered these
individuals into about 50 communities using standard
Hi Collin,
Have a look at this:
http://stats.stackexchange.com/questions/70643/power-analysis-for-kruskal-wallis-or-mann-whitney-u-test-using-r
Although, thinking about it, this might have constituted your "perusal of
the literature".
Plus it always looks better when you spell the names properly
Please stop... you are acting like a broken record, and are also posting in
HTML format. Please read the Posting Guide and demonstrate that you have used a
search engine on this topic before posting again.
---
Jeff Newmiller
Greetings, I am working on a project where we are applying the
Kruskal-Wallace test to some factor data to evaluate their correlation with
existing grade data. I know that the grade data is nonnormal therefore we
cannot rely on ANOVA or a similar parametric test. What I would like to
find is a me
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