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From |
Gabriel Nelson <lgabrielnelson@gmail.com> |

To |
statalist <statalist@hsphsun2.harvard.edu> |

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 might lie within -sktest-. I won't worry any more about this 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 >>> 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. > * > * 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/ -- Gabriel Nelson Doctoral Candidate Dept. of Sociology University of California- Los Angeles http://www.soc.ucla.edu/people/graduate-student?lid=4344 * * 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/

**Follow-Ups**:**Re: st: ladder question for right-skewed variable***From:*David Hoaglin <dchoaglin@gmail.com>

**References**:**st: ladder question for right-skewed variable***From:*Gabriel Nelson <lgabrielnelson@gmail.com>

**Re: st: ladder question for right-skewed variable***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: ladder question for right-skewed variable***From:*Gabriel Nelson <lgabrielnelson@gmail.com>

**Re: st: ladder question for right-skewed variable***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: ladder question for right-skewed variable***From:*Gabriel Nelson <lgabrielnelson@gmail.com>

**Re: st: ladder question for right-skewed variable***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: ladder question for right-skewed variable***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: ladder question for right-skewed variable***From:*Nick Cox <njcoxstata@gmail.com>

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