[R] count ties after rank?

[David Winsemius]

On Nov 21, 2011, at 3:20 PM, Hao, Zhaozhe wrote:

> Hi,
>
>   Thank you all for the the quick response. But there still some  
> questions.
>
>    1) nTies = length(x) - length(unique(x)) cannot distinguish  
> vector (1,2,2,2,3), and (1,2,2,3,3)....
>
>     2) table(x)[table(x) >1] tells me the right number,  but how can  
> I call the numbers from the result of table function? I.e., I want  
> to get the tie number, t and use it in another equation.

This will give you a named one ros matrix, Can e address by location  
or name.

 > x <- sample(c(1:10,NA,NA), 20, replace=TRUE)
 > ties <- table(x)[table(x) >1]
 >
 > ties
x
1 2 3 4 5 8
2 3 2 2 2 3
 > ties[1]
1
2
 > ties["8"]    # note that this is really 3 and that the 8 is a name
8
3

 > names(ties)
[1] "1" "2" "3" "4" "5" "8"
 > unname(ties)
[1] 2 3 2 2 2 3

>
>
> ________________________________________
> From: David Winsemius [dwinsemius at comcast.net]
> Sent: Monday, November 21, 2011 14:05
> To: Peter Langfelder
> Cc: Hao, Zhaozhe; r-help at r-project.org
> Subject: Re: [R] count ties after rank?
>
> On Nov 21, 2011, at 2:50 PM, Peter Langfelder wrote:
>
>> On Mon, Nov 21, 2011 at 11:36 AM, Hao, Zhaozhe <haozhaozhe at ou.edu>
>> wrote:
>>> Hello!
>>>
>>>   I need to use Kruskal-Wallis test and post-hoc test (Dunn's
>>> test) for my data. But when I searched around, I only found this
>>> function: kruskal.test. But nothing for Dunn's test.
>>>
>>>   So I started to write one myself.  But  I do not know how to
>>> count ties in the data frame. I can use for loops but it seems long
>>> and unnecessary since the rank function actually knows the ties. So
>>> can anyone give me a hint on how I can "count" the number of ties?
>>>
>>
>> If you just need the overall number of ties in a vector (say x), you
>> can get it by
>>
>> nTies = length(x) - length(unique(x))
>
> And if you wnat to know which ties are which you can do:
>
> table(x)[table(x) > 1]
>
>>
>> This will not work if missing data are present, so you will have to
>> remove those first.
>>
>
> table() would ignore missing data (assuming it were properly NA not-
> valued).
>
>> x <- sample(c(1:10,NA,NA), 20, replace=TRUE)
>> table(x)[table(x) >1]
> x
>  1  4  7  9 10
>  2  3  2  2  2
>> x
>  [1]  7 10  3  9 NA NA  2  4 10 NA NA  4  5  9  6  4  1  1  7  8
>
> --
>
> David Winsemius, MD
> West Hartford, CT
>

David Winsemius, MD
West Hartford, CT