Thank you so much for your response and apologies for the delay in responding. 
This has been helpful.


I have been looking into several other options over the last few weeks and been 
talking with statisticians as well and SAS seems to have some options that work 
for survey type of data. At this point, I am still exploring R and if I am able 
to provide additional feedback on this issue, I will post. I really appreciate 
your help. Thank you.


________________________________
From: Abby Spurdle <spurdl...@gmail.com>
Sent: July 27, 2019 9:39:49 PM
To: Mavra Ahmed <mavz.ah...@utoronto.ca>
Cc: r-help@r-project.org <r-help@r-project.org>
Subject: Re: [R] Survey Data - comparing multiple groups

> I would like to be able to get p-values between the groups and be able to 
> adjust for multiple comparisons and would appreciate if someone can guide me.

> I did convert my df into a svrepdesign object as follows:
> > df<-svrepdesign (data=df1, scale=1, repweights = df1[, 496:995], 
> > type="BRR", combined.weights=TRUE, weight=~WTS_P, na.rm=TRUE)
>  therefore, I have called "df" as a svrepdesign however, from the survey 
> package, the svyranktest is shown  with svydesign only and not with 
> svrepdesign. I could consider using other tests such as svyglm but am also 
> having issues with it.

This is outside my area.
However, as no one else as responded, I'll offer some more comments.

Firstly, I'm doubtful the Kruskal Wallis test will give you pairwise p-values.
There are other tests for this.
It's also possible to create a matrix of p-values, using an arbitrary test, and 
then use a correction method.
However, I recommend caution with this.

Secondly, I don't know if the svyranktest() function will work with the 
svrepdesign object or not.
However, if not, I would assume that other functions in the package would work.

Either way, I'm wondering if you've created the svrepdesign object correctly.
In particular, "weight=~WTS_P" would assign a formula to weights, probably, 
which doesn't sound right.
And "repweights = df1[, 496:995]" looks questionable, because one, is has 
hundreds of columns, and two, you've already given that data in the first 
argument.



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