{smcl}
{* *! version 1.0.0 18Aug2023}{...}
{title:Title}
{p2colset 5 21 22 2}{...}
{p2col:{hi:mountainplot} {hline 2}} Folded Empirical Distribution Function Curves (Mountain Plots) {p_end}
{p2colreset}{...}
{marker syntax}{...}
{title:Syntax}
{p 8 14 2}
{cmd:mountainplot}
{it:{help varlist:varlist}}
{ifin}
[,
{opt diff:erence}
{opt st:andardize}
{cmd:}{it:{help twoway_options:twoway_options}}
]
{synoptset 16 tabbed}{...}
{synopthdr:mountainplot}
{synoptline}
{synopt:{opt diff:erence}}generates the mountainplot by computing the difference(s) between the first variable specified in {it:varlist} and all others {p_end}
{synopt:{opt st:andardize}}standardizes values of {it:varlist} to have a mean of 0 and standard deviation of 1 {p_end}
{synopt:{it:{help twoway_options:twoway_options}}}allows all available options of {help twoway_options:twoway graphs}{p_end}
{synoptline}
{p2colreset}{...}
{p 4 6 2}
{marker description}{...}
{title:Description}
{pstd}
{opt mountainplot} produces mountain plots for variables in a {it:varlist} as proposed by Monti (1995), and
mountain plots for differences between variables in a {it:varlist} as proposed by Krouwer and Monti (1995).
A mountainplot is a graphical representation of an empirical cumulative distribution function in which
percentile values above 50 are "folded" (i.e. subtracted from 100 in order to produce a reverse ordering). The resulting
graphic resembles a mountain where the peak is approximately at the median.
{pstd}
Monti (1995) suggests that mountain plots allow the user to perform the following:{p_end}
{pstd}
1. Determine the median.{p_end}
{pstd}
2. Determine the range.{p_end}
{pstd}
3. Determine the central or tail percentiles of any magnitude.{p_end}
{pstd}
4. Observe outliers.{p_end}
{pstd}
5. Observe unusual gaps in the data.{p_end}
{pstd}
6. Examine the data for symmetry.{p_end}
{pstd}
7. Compare several distributions.{p_end}
{pstd}
8. Visually gauge the sample size.{p_end}
{pstd}
Krouwer and Monti (1995) propose creating a mountain plot as a complementary to the Bland and Altman plot (Bland and Altman 1986).
Here, the mountain plot represents the percentile difference between a new test and a reference test (Y-axis).
This percentile difference is then plotted against the difference between the two tests (X-axis).
{title:Options}
{p 4 8 2}
{cmd:difference} generates the mountain plot by computing the difference(s) between the first variable specified in {it:varlist} which should
represent the reference test (gold standard) and all other variables in the {it:varlist} (representing one or more new tests). The default
is to generate the mountain plot for each variable specified in {it:varlist} and plot it against that variable's range of values
on its original scale.
{p 4 8 2}
{cmd:standardize} transforms the data in {it:varlist} to have a mean of 0 and standard deviation of 1. This option is
particularly appropriate when more than one variable is being plotted and the variables are on different scales.
{p 4 8 2}
{cmd:{it:{help twoway_options:twoway_options}}} allows all available options for twoway graphs.
{title:Examples}
{pstd}Setup {p_end}
{phang2}{cmd:. use lungfunction.dta}{p_end}
{pstd} Produce a mountain plot for the first of four measurements of lung function in each of 20 schoolchildren (data are from Bland & Altman [1996]) {p_end}
{phang2}{cmd:. mountainplot rating1}{p_end}
{pstd} Now plot all four measurements of lung function {p_end}
{phang2}{cmd:. mountainplot rating1 - rating4}{p_end}
{pstd}Same as above but used standardized values for the specified variables {p_end}
{phang2}{cmd:. mountainplot rating1 - rating4, stand}{p_end}
{pstd}Produce a mountain plot of the difference between rating1 (reference test) and rating2 (a new test) {p_end}
{phang2}{cmd:. mountainplot rating1 rating2, difference}{p_end}
{pstd}Same as above, but plot the difference between rating1 (reference test) and all other ratings (new tests) {p_end}
{phang2}{cmd:. mountainplot rating1 - rating4, difference}{p_end}
{title:References}
{p 4 8 2}
Bland, J. M., and D. G. Altman. 1986. Statistical method for assessing agreement between two methods of clinical measurement.
{it:Lancet} 327: 307-310.
{p 4 8 2}
Bland, J. M., and D. G. Altman. 1996. Statistics notes: measurement error.
{it:British Medical Journal} 312: 1654.
{p 4 8 2}
Krouwer, J. S. and K. L. Monti 1995. A simple, graphical method to evaluate laboratory assays.
{it:European Journal of Clinical Chemistry and Clinical Biochemistry} 33: 525-527.
{p 4 8 2}
Monti, K. L. 1995. Folded empirical distribution function curves-mountain plots.
{it:The American Statistician} 49: 342–345.
{marker citation}{title:Citation of {cmd:mountainplot}}
{p 4 8 2}{cmd:mountainplot} is not an official Stata command. It is a free contribution
to the research community, like a paper. Please cite it as such: {p_end}
{p 4 8 2}
Linden A. (2023). MOUNTAINPLOT: Stata module to produce folded empirical distribution function curves (mountain plots)
{title:Authors}
{p 4 4 2}
Ariel Linden{break}
President, Linden Consulting Group, LLC{break}
alinden@lindenconsulting.org{break}
{title:Also see}
{p 4 8 2} Online: {helpb cumul}, {helpb mountain} (if installed){p_end}