Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.
[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]
Re: st: ladder question for right-skewed variable
From
Nick Cox <[email protected]>
To
"[email protected]" <[email protected]>
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
[email protected]
On 26 April 2013 19:17, Nick Cox <[email protected]> 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
> [email protected]
>
>
> On 26 April 2013 18:55, Gabriel Nelson <[email protected]> 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 <[email protected]> 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.
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/faqs/resources/statalist-faq/
* http://www.ats.ucla.edu/stat/stata/