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Re: st: ladder question for right-skewed variable


From   Nick Cox <njcoxstata@gmail.com>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: ladder question for right-skewed variable
Date   Fri, 26 Apr 2013 19:20:19 +0100

Just to underline that kurtosis in your variable was calculated by
-summarize- 108. That's BIG. No wonder -sktest- can't cope.
Nick
njcoxstata@gmail.com


On 26 April 2013 19:17, Nick Cox <njcoxstata@gmail.com> wrote:
> That's not quite "no transformations appeared in the output" as
> -ladder- is signalling P-values for some cases.
>
> But I readily agree that -ladder- is not doing a good job here at all.
>
> In fact, I am now reminded of evident -ladder- problems shown in a
> recent thread starting at
> http://www.stata.com/statalist/archive/2013-02/msg00862.html
>
> I can't find a public email, even though I thought I posted on this,
> but my impression from looking at the code is that -ladder- is
> essentially fragile. The real problem here is within -sktest-. It can
> break down, it seems, for large sample sizes and/or large deviations
> from Gaussianity. Then it bounces back missings.
>
> I think you just need to abandon -ladder-. It's not essential. You
> don't need _any_ test to tell you that some transformation will help
> if the goal is to reduce asymmetry, and there are only a few credible
> alternatives.
>
> As David and I pointed out, log transformation should work quite well
> for your data,
>
> but but but: (my suggestion; David may not agree) why transform at
> all? Your solutions start with -poisson- (or, for consenting adults,
> -nbreg-).
>
> BTW, -ladder- is a command, not a function, and in Stata ne'er the
> twain shall meet.
>
> Nick
> njcoxstata@gmail.com
>
>
> On 26 April 2013 18:55, Gabriel Nelson <lgabrielnelson@gmail.com> wrote:
>> Thanks Nick, yes exactly, my question is why the ladder function fails
>> to provide any chi-square values here. I'll attach the Stata output
>> here:
>>
>> . ladder disp_2000
>>
>> Transformation         formula               chi2(2)       P(chi2)
>> ------------------------------------------------------------------
>> cubic                  dis~2000^3                 .            .
>> square                 dis~2000^2                 .            .
>> identity               dis~2000                   .            .
>> square root            sqrt(dis~2000)             .        0.000
>> log                    log(dis~2000)              .        0.000
>> 1/(square root)        1/sqrt(dis~2000)           .        0.000
>> inverse                1/dis~2000                 .        0.000
>> 1/square               1/(dis~2000^2)             .        0.000
>> 1/cubic                1/(dis~2000^3)             .        0.000
>>
>> . sum disp_2000, detail
>>
>>       Number displaced 2000 (if data unavailable go up
>>                            to 2003
>> -------------------------------------------------------------
>>       Percentiles      Smallest
>>  1%            1              1
>>  5%            2              1
>> 10%            3              1       Obs                1010
>> 25%            6              1       Sum of Wgt.        1010
>>
>> 50%         15.5                      Mean           281.5297
>>                         Largest       Std. Dev.      1217.168
>> 75%           82           9421
>> 90%        436.5           9505       Variance        1481497
>> 95%         1251          16255       Skewness       9.012044
>> 99%         5953          19569       Kurtosis       108.8061
>>
>> On Fri, Apr 26, 2013 at 10:47 AM, Nick Cox <njcoxstata@gmail.com> wrote:
>>> Please see my answers too. You have still not given the exact -ladder-
>>> command you used or its output, so it is really difficult to know what
>>> is going on.
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