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# Re: st: ladder question for right-skewed variable

 From Gabriel Nelson To statalist Subject Re: st: ladder question for right-skewed variable Date Fri, 26 Apr 2013 13:57:16 -0700

```Thanks very much for your suggestions Nick. It makes sense that the
problem and just proceed with the qnorm command, as you suggested.
Thanks again.

Gabriel

On Fri, Apr 26, 2013 at 11:45 AM, Nick Cox <njcoxstata@gmail.com> wrote:
> Three assertions based on a mix of experience and prejudice:
>
> 1. The best way to check for normality is with -qnorm-. Even if
> normality is not your reference case, asymmetry will show up clearly
> on a -qnorm- graph.
>
> 2. 90% of the time, choosing transformations boils down to whether
> three possible transformations are any use, root, logarithm or
> reciprocal.
>
> 3. So, do-it-yourself is easy:
>
> gen rtmyvar = sqrt(myvar)
> gen logmyvar = log(myvar)
> gen recmyvar = 1/myvar
>
> qnorm myvar, name(a)
> qnorm rtmyvar, name(b)
> qnorm logmyvar, name(c)
> qnorm recmyvar, name(d)
>
> Not universally known fact: Giving a name to a graph means that it
> sticks around until _you_ close it. So, you have four graphs on your
> monitor. Arrange them with your mouse so you can compare. Usually it's
> easy to pick what works best, without any formal machinery.
>
> (Yes, I know about -gladder-, but this is simpler in practice.)
>
>
> Nick
> njcoxstata@gmail.com
>
>
> On 26 April 2013 19:20, Nick Cox <njcoxstata@gmail.com> wrote:
>> 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
>>> 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
>>>
>>> but but but: (my suggestion; David may not agree) why transform at
>>> -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:
>>>>
>>>>
>>>> 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:
>>>>> command you used or its output, so it is really difficult to know what
>>>>> is going on.
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--
Gabriel Nelson
Doctoral Candidate
Dept. of Sociology
University of California- Los Angeles