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From |
Paul Seed <paul.seed@kcl.ac.uk> |

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

Subject |
Re: st: Skewness estimates with svyset data |

Date |
Mon, 17 Nov 2008 12:54:58 +0000 |

Dear Richard Palmer-Jones, Statalist,

depending on the age.

know he has some growth data on measurements repeated at regular intervals.

would be very grateful. Paul T Seed MSc CStat, Lecturer in Medical Statistics, tel (+44) (0) 20 7188 3642, fax (+44) (0) 20 7620 1227 Wednesdays: (+4) (0) 20 7848 4148 paul.seed@kcl.ac.uk, paul.t.seed@gmail.com King's College London, Division of Reproduction and Endocrinology St Thomas' Hospital, Westminster Bridge Road, London SE1 7EH

Date: Sun, 16 Nov 2008 18:22:30 +0000 From: "Richard Palmer-Jones" <richard.palmerjones@gmail.com> Subject: Re: st: Skewness estimates with svyset data Thanks for this. I had to do something else for afew days, then got the papers from ILL (our library did not have them) and I corresponded with Cole and his co-author, who clarifed the original papers (Cole 1990, The LMS Method for constructing normalised growth curves, European Journal of Clinical Nutrition, 44, 45-60, makes things clear). They also pointed me to the two (MS excel add-in) progammes they have published LMSGrowth and LMSChartmaker - the latter not being immediately obvious). LMSChartmaker allows you to input raw height, weight etc., and age data and compute L, M, and S curves that can be input to LMSGrowth. M is the median, S the coefficient of variation and L the Cox-Box Power used to transform the indicator variable at each age. From a casual reading I see that these parameters are constrained to be smoothly related to their neighbours. M<y calculations of LMS suggest that L for adults is not constant at 1 (normal) over ages, but I need to onfirm this, even though I see that the L variable in LMSChartmaker for height is 1. Strange. Weight is certainly not normal. I agree that nlcom does not seem a reliable way to calculate skewness (= 3rd moment). They also directed me to Rigby and Stasinopoulous, 2005, Generalized additive Models for location, scale and shape, Applied Statistics, 54, pt. 3, 507-554, for a similar approach with a R suite of programmes, which I have yet to explore and might be worth porting to Stata.. As soon as I get time I hope to produce my LMS parameters, and then "z-scores" using LMSChartmaker, which will go back into Stata. It should be possible to use the LMS parameters to extend thier zanthro Stata ado file to enable that to be used beyond the age of 20 (USA flavour) or 23 (UK flavour), but that is some way down the road. I did a rough work through my data late one night which suggested that whether one uses the standard (not adjusted fro skewness) z-scores of males and females from USA data, or used the LMS z-scores I computed from those data, there is no good reason to thing heights of Indian males increased any faster (in tedrms of z-scores) than those of Indian women, in fact, rather the reverse. But this needs more work. .Richard On Wed, Nov 5, 2008 at 1:22 PM, Nick Cox <n.j.cox@durham.ac.uk> wrote:First, I think you need to keep explaining for the benefit of anyone trying to pick up on this thread that LMS refers to a method devised by [Timothy J.] Cole and others for handling growth curves. You earlier gave a reference that was just Cole et al. 2008. Despite a strong hint earlier from Stas Kolenikov, the further details of that reference are still outstanding. One of my dictionaries explains LMS as London Mathematical Society, London Missionary Society, and London, Midland and Scottish Railway. It is easy to guess that none of those apply but not so obvious that LMS here does _not_ mean Least Median of Squares as devised by Rousseeuw, as many statistically-minded people might imagine. Rousseeuw, P.J. 1984. Least median of squares regression. Journal, American Statistical Association 79: 871-880. The more general point, which should be obvious except that many list members act as if it were not true, is that the list includes people from several quite different disciplines. Hence if you want to maximise the readership of a question some explanations help a lot and rarely do harm. In terms of what you want to do: Several people on this list should know much, much more about Cole's method than I do but they are keeping quiet. I am surprised at the implication that you need to feed skewness to Cole's method. That is not, in particular, the case for -colelms- from SSC. I understood that Cole's method was in essence designed to work well with the possibly skew distributions that do occur and as such there is no specific need to prepare the data or satisfy the assumptions of the method, as there aren't any, except I guess that ages are accurate and size measurement error negligible. On the other hand, it may be that the missing reference, Cole et al. 2008, gives a quite different twist to the method, but then we are back to my earlier point. In general ignoring some fraction of data in the tail seems a very bad idea unless it is obvious that the values concerned are all untrustworthy. Even them some sensitivity analysis (with outliers vs without outliers) would seem advisable. Nick n.j.cox@durham.ac.uk Richard Palmer-Jones Yes, I have been planning to use LMS method - basically adding the adult parameters to the child hood ones given there. LMS needs skewness - hence my interest. I am only interested in the adults older that 25 (when both males and females have reached their full height) so complicated smoothing is not necessary. Yes, NHANES has heavy weighting which makes a considerable difference to estimates (and false PSUs). However, since the skewness reported by summarize is positive in adults I am wondering whether a simpler procedure is to truncate the parameter for valuies > 2.5sd, or to transform to logs, or some such and work in them. Unfortunately ln(weight) is also skewed.Stas Kolenikov To Nick: yes, I've used skewness and kurtosis to test for normality a bunch of times (and there's a famous Mardia's multivariate generalization that I programmed up :)). But frankly I personally don't remember seeing confidence intervals on skewness anywhere at all. Estimation and testing are two related ways of looking at data with statistics, but with skewness and kurtosis you really estimate something to see that it is close enough to zero... and sometimes you don't even estimate a thing and go straight to the test statistic.*

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**Follow-Ups**:**Re: st: Skewness estimates with svyset data***From:*"Richard Palmer-Jones" <richard.palmerjones@gmail.com>

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