-------------------------------------------------------------------------------
help for parplot
-------------------------------------------------------------------------------

Parallel coordinates plots

        parplot varlist [if exp] [in range] [ , by(byvar [, suboptions])
                 horizontal over(varname) transform(transform) variablelabels
                 plot(plot) addplot(plot) graph_options ]


Description

    parplot produces a parallel coordinates plot of varlist.  Each variable
    is plotted on a separate vertical or horizontal scale and the values for
    each observation are shown by connected line segments.  An observation
    will be ignored if it has missing values for any variable in varlist (and
    by default, if by() is specified, for byvar).

    Such plots have a long history under various guises. Wegman (1990) gave a
    definitive account for a statistical readership.  Cooke and van Noortwijk
    (2000) discuss their use, under the name cobweb plots, in sensitivity
    analysis.  Robbins (2005) gives examples in an introductory text.
    Andrienko and Andrienko (2005) give further examples and extensions.


Options 

    by() specifies that a separate plot should be drawn for each value of
        byvar. See help on by_option and note, among other possibilities, the
        suboptions total and missing.

    horizontal draws variable scales horizontally. The default is vertical.

    over() specifies a variable to be used to identify different categories.
        Different pens will be used for different categories, and it is
        possible to specify different marker symbols, line patterns, and so
        forth.

    transform() specifies a transformation to be applied to each variable
        before plotting. Each transformation may be specified by as little as
        one letter, m, c, s or r.

        maxmin specifies transforming to (value - minimum) /
        (maximum - minimum), which is the default. Values shown thus vary
        from 0 to 1.

        centered or centred specifies transforming to (value - median) /
        max(maximum - median, median - minimum).  Each median thus is shown
        at 0 and values shown vary from (possibly) -1 to (possibly) 1. Note
        that transformed values for any given variable will attain both -1
        and 1 if and only if maximum - median = median - minimum. This
        transform was used by Gleason (1996).

        standardized or standardised specifies transforming to
        (value - mean) / SD. Each mean thus is shown at 0.

        raw specifies no transform, i.e. data are shown as supplied.  This
        may be a good choice for variables expressed in the same units.

    variablelabels specifies that multiple variables be labelled by their
        variable labels. The default is to use variable names.

    plot(plot) provides a way to add other plots to the generated graph; see
        help plot option. (Stata 8 only.)

    addplot(plot) provides a way to add other plots to the generated graph;
        see help addplot option.  (Stata 9 up.)

    graph_options are options of twoway connected.

       
Examples 

    . sysuse census, clear

    . foreach v in death divorce marriage {
    .         gen r_`v' = log10(`v' / pop)
    . }

    . foreach t in maxmin centred standardised raw {
    .         parplot r_* , tr(`t') by(region, caption(logarithmic scales)
                title(US states 1980) t1(`t' scaling)) hor yla(1 "deaths" 2
                "divorces" 3 "marriages", ang(h))
    .         more
    . }

    . sysuse auto, clear
    . gen gpm = 1 / mpg
    . parplot gpm weight disp, xsc(r(0.8 3.2)) yla(, ang(h)) over(foreign)
        ms(oh dh) clp(_ ".#")
    . parplot gpm weight disp, hor ysc(r(0.8 3.2)) yla(, ang(h))
        over(foreign) ms(oh dh) clp(_ ".#")


Acknowledgments

    The parcoord program written by John R. Gleason for Stata 4 (Gleason
    1996) was a most valuable start for this program.

    Vince Wiggins made very helpful comments. Ian S. Evans supplied the
    Andrienko reference. Scott Merryman found a bug. Garry Anderson provoked
    an update of the help.


Author

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


References

    Andrienko, G. and N. Andrienko. 2005. Blending aggregation and selection:
        adapting parallel coordinates for the visualization of large
        datasets. Cartographic Journal 42: 49-60.

    Cooke, R.M. and J.M. van Noortwijk. 2000. Graphical methods.  In
        Santelli, A., K. Chan and E.M. Scott (eds) Sensitivity analysis.
        Chichester: John Wiley, 245-264.

    Gleason, J.R. 1996. Graphing high-dimensional data using parallel
        coordinates. Stata Technical Bulletin 29: 10-14 (STB Reprints 5:
        53-60).

    Robbins, N.B. 2005. Creating More Effective Graphs.  Hoboken, NJ: Wiley.

    Wegman, E.J. 1990. Hyperdimensional data analysis using parallel
        coordinates. Journal, American Statistical Association 85: 664-675.