[R] Crosstable-like analysis (ks test) of dataframe

[Johannes Radinger]

Thank you Rui!

that works as I want it... :)

/Johannes

On Fri, Sep 28, 2012 at 12:30 PM, Rui Barradas <ruipbarradas at sapo.pt> wrote:
> Hello,
>
> Try the following.
>
>
> f <- function(x, y, ...,
>         alternative = c("two.sided", "less", "greater"), exact = NULL){
>     #w <- getOption("warn")
>     #options(warn = -1)  # ignore warnings
>     p <- ks.test(x, y, ..., alternative = alternative, exact =
> exact)$p.value
>     #options(warn = w)
>     p
> }
>
> n <- 1e1
> dat <- data.frame(X=rnorm(n), Y=runif(n), Z=rchisq(n, df=3))
>
> apply(dat, 2, function(x) apply(dat, 2, function(y) f(x, y)))
>
> Hope this helps,
>
> Rui Barradas
> Em 28-09-2012 11:10, Johannes Radinger escreveu:
>>
>> Hi,
>>
>> I have a dataframe with multiple (appr. 20) columns containing
>> vectors of different values (different distributions).
>>   Now I'd like to create a crosstable
>> where I compare the distribution of each vector (df-column) with
>> each other. For the comparison I want to use the ks.test().
>> The result should contain as row and column names the column names
>> of the input dataframe and the cells should be populated with
>> the p-value of the ks.test for each pairwise analysis.
>>
>> My data.frame looks like:
>> df <- data.frame(X=rnorm(1000,2),Y=rnorm(1000,1),Z=rnorm(1000,2))
>>
>> And the test for one single case is:
>> ks <- ks.test(df$X,df$Z)
>>
>> where the p value is:
>> ks[2]
>>
>> How can I create an automatized way of this pairwise analysis?
>> Any suggestions? I guess that is a quite common analysis (probably with
>> other tests).
>>
>> cheers,
>> Johannes
>>
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>
>