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From |
"Nick Cox" <[email protected]> |

To |
<[email protected]> |

Subject |
st: -qlognorm- package available on SSC |

Date |
Mon, 5 Aug 2002 18:07:56 +0100 |

I am guessing that, despite the advent of -ssc whatsnew- on 2 August 2002, announcements of packages new or substantially revised on SSC are still of some interest. Thanks to Kit Baum, a package -qlognorm- containing -qlognorm- and -plognorm- programs has been added to SSC. Summary ======= -qlognorm- is a package for diagnostic plots for the lognormal distribution, somewhat in the spirit of official Stata's -qnorm-, etc. -qlognorm- plots the quantiles of varname against the quantiles of the corresponding lognormal distribution. -plognorm- graphs a standardized lognormal probability plot for varname. The (two-parameter) lognormal distribution fitted to varname corresponds to a normal distribution with the mean and standard deviation of log(varname). Stata 7 is required. Details ======= Sometimes there is interest in whether the lognormal is appropriate as a distribution model for a variable. Other times there is interest in whether the logarithm of a variable is more nearly normal than that variable itself. These are two sides of the same question. -qlognorm- and -plognorm- are commands for investigating it directly. With official Stata, it is easy to generate a new variable which is the logarithm of a variable and then to use -qnorm- and -pnorm- to see whether that new variable is close to normal in distribution. Using -qlognorm- and -plognorm- instead has these small but distinct advantages: 1. If you do this frequently, you will need to type less; sometimes, but not always, you will decide that a log transformation is advisable. 2. Fit can be assessed graphically on both raw and transformed scales. 3. If desired, you can use a plotting position other than the i / (N + 1) wired into -qnorm- and -pnorm-. There is a modest literature on choice of plotting positions in probability plots, and some grounds for choosing positions other than what official Stata has chosen (somewhat arbitrarily, so far as I can tell). For a little more discussion, see http://www.stata.com/support/faqs/stat/pcrank.html 4. If desired, you can insist on maximum likelihood estimation. That is, the standard deviation used by default is that emitted by -summarize-, which is the square root of the variance estimated with (N - 1) as divisor, which is not the maximum likelihood estimator. This option is for purists: if the difference between N and (N - 1) makes a difference to your results, this should be resolved by a bigger sample, not by standing on principle. Still, some people like being purists. Nick [email protected] * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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