[R] count ties after rank?
David Winsemius
dwinsemius at comcast.net
Mon Nov 21 21:30:15 CET 2011
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
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