{smcl} {* 5august2002}{...} {hline} help for {hi:qlognorm, plognorm} {hline} {title:Distributional diagnostic plots (lognormal distribution)} {p 8 16}{cmd:qlognorm} {it:varname} [{cmd:if} {it:exp}] [{cmd:in} {it:range}] [{cmd:,} {cmd:grid} {it:graph_options} {cmd:a(}{it:#}{cmd:)} {cmd:ml} ] {p 8 16}{cmd:plognorm} {it:varname} [{cmd:if} {it:exp}] [{cmd:in} {it:range}] [{cmd:,} {cmdab:g:rid} {it:graph_options} {cmd:a(}{it:#}{cmd:)} {cmd:ml} ] {title:Description} {p}{cmd:qlognorm} plots the quantiles of {it:varname} against the quantiles of the corresponding lognormal distribution (Q-Q plot). {p}{cmd:plognorm} graphs a standardized lognormal probability (P-P) plot for {it:varname}. {p}The (two-parameter) lognormal distribution fitted corresponds to a normal distribution with the mean and standard deviation of log({it:varname}). {title:Remarks} {p}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. {cmd:qlognorm} and {cmd:plognorm} are commands for investigating it directly. {p}With official Stata, it is easy to {cmd:generate} a new variable which is the logarithm of a variable and then to use {cmd:qnorm} and {cmd:pnorm} to see whether that new variable is close to normal in distribution. Using {cmd:qlognorm} and {cmd:plognorm} instead has these small but distinct advantages: {p 4 4}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. {p 4 4}2. Fit can be assessed graphically on both raw and transformed scales. {p 4 4}3. If desired, you can use a plotting position other than the i / (N + 1) wired into {cmd:qnorm} and {cmd:pnorm}. {p 4 4}4. If desired, you can insist on maximum likelihood estimation. {title:Options} {p 0 4}{cmd:grid} adds grid lines at the .05, .10, .25, .50, .75, .90, and .95 quantiles when specified with {cmd:qlognorm}. It is equivalent to {cmd:yline(.25 .5 .75) xline(.25 .5 .75)} when specified with {cmd:plognorm}. {p 0 4}{it:graph_options} are any of the options allowed with {cmd:graph, twoway}; see help {help grtwoway}. {p 0 4}{cmd:a(}{it:#}{cmd:)} specifies a family of plotting positions, defined by {bind:(i - a)}{bind: / (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 {cmd:qnorm} and {cmd:pnorm} is 0. Choice of {cmd: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 {browse "http://www.stata.com/support/faqs/stat/pcrank.html":http://www.stata.com/support/faqs/stat/pcrank.html}. {p 0 4}{cmd: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({it:varname}) is calculated as the root mean square deviation from the mean. Multiplying the default standard deviation, which is that produced by {cmd:summarize}, by a factor of {bind:sqrt(N / (N - 1))} is rarely material unless the sample size is very small, and then the exercise is moot whatever is done. {title:Examples} {p 4 8}{inp:. qnorm mpg} {p 4 8}{inp:. qlognorm mpg}{p_end} {p 4 8}{inp:. qlognorm mpg, xlog ylog} {p 4 8}{inp:. plognorm mpg} {title:Author} Nicholas J. Cox, University of Durham, U.K. n.j.cox@durham.ac.uk {title:Also see} {p} Manual: {hi:[R] diagplots}, {hi:[R] summarize}{p_end} {p}On-line: help for {help diagplots}, {help graph}