{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}