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help for qlognorm, plognorm
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Distributional diagnostic plots (lognormal distribution)

qlognorm varname [if exp] [in range] [, grid graph_options a(#) ml ]

plognorm varname [if exp] [in range] [, grid graph_options a(#) ml ]

Description

qlognorm plots the quantiles of varname against the quantiles of the
corresponding lognormal distribution (Q-Q plot).

plognorm graphs a standardized lognormal probability (P-P) plot for varname.

The (two-parameter) lognormal distribution fitted corresponds to a normal
distribution with the mean and standard deviation of log(varname).

Remarks

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

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.

4. If desired, you can insist on maximum likelihood estimation.

Options

grid adds grid lines at the .05, .10, .25, .50, .75, .90, and .95 quantiles
when specified with qlognorm. It is equivalent to yline(.25 .5 .75)
xline(.25 .5 .75) when specified with plognorm.

graph_options are any of the options allowed with graph, twoway; see help
grtwoway.

a(#) specifies a family of plotting positions, defined by
(i - a) / (N - 2a + 1), where i is the rank assigned to an observed value
and N is the number of observed values. The default is 0.5.  (Note that the
default for qnorm and pnorm is 0.  Choice of a is rarely material unless
the sample size is very small, and then the exercise is moot whatever is
done. For more on plotting positions, see
http://www.stata.com/support/faqs/stat/pcrank.html.

ml specifies maximum likelihood estimation. This option is for purists only.
The only difference it makes is to ensure that the standard deviation of
log(varname) is calculated as the root mean square deviation from the mean.
Multiplying the default standard deviation, which is that produced by
summarize, by a factor of sqrt(N / (N - 1)) is rarely material unless the
sample size is very small, and then the exercise is moot whatever is done.

Examples

. qnorm mpg

. qlognorm mpg

. qlognorm mpg, xlog ylog

. plognorm mpg

Author

Nicholas J. Cox, University of Durham, U.K.
n.j.cox@durham.ac.uk

Also see

Manual:  [R] diagplots, [R] summarize
On-line:  help for diagplots, graph

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