help for linkplot

Linked scatter plots

linkplot
yvarlist
xvar
[if exp]
[in range]
[weight]
,
link(linkvar)
[
asyvars
cmissing({y|n})
sort(sort_varlist)
plot(plot)
addplot(plot)
graph_options
]

Description

linkplot plots yvarlist versus xvar such that data
points are linked (i.e. connected) within groups defined by distinct values of
linkvar. For example, with paired data it might be desired to link each
pair, or with panel data it might be desired to link observations within each
panel.

aweights, fweights and pweights are allowed;
see help weights.

Options

link(linkvar) specifies that values of each
variable in yvarlist are to be linked if they have the same value
of linkvar. This option is required.

asyvars specifies that the groups defined by linkvar
should be plotted as if they were separate y variables.

cmissing({y|n}) specifies
whether missing values are ignored. The default is to ignore missing values.
Note that this is a slight variant on cmissing() as explained at help
connect_options. The only allowed arguments are y, to ignore, and
n, not to ignore. These are automatically expanded to all variables
plotted.

sort(sort_varlist) specifies that values are to be
linked in order of sort_varlist. By default values are linked in sort
order of xvar, the last variable specified before the option comma.
Do not specify linkvar, as this will automatically be used.

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

Box, Hunter and Hunter (1978, p.100) gave data for 10 boys on the
wear of shoes made using materials A and B. The data are also analysed by
Wild and Seber (2000, p.446) and Davison (2003, pp.421-3). The units are not
specified. Before exemplifying
linkplot we make some general comments on graphing such paired data.
One natural data structure would be something like this:

A B id
13.2 14.0 1
8.2 8.8 2
10.9 11.2 3
14.3 14.2 4
10.7 11.8 5
6.6 6.4 6
9.5 9.8 7
10.8 11.3 8
8.8 9.3 9
13.3 13.6 10

This data structure permits some Stata graphs, but inhibits others.
A scatter plot such as scatter A B may be useful, but does not allow easy
decoding of the difference, say A - B, which is here of central
interest. Similarly, it is difficult to read off ratios such as
A / B. If A and B are plotted versus id,
or vice versa, the resulting graphs suffer from the arbitrariness of
id. See also the possibilities offered by pairplot, which
may be installed using ssc.

Other possibilities are available after a reshape:

. rename A wearA

. rename B wearB

. reshape long wear, string i(id) j(j)

. encode j, gen(material)

Now we have a choice, including

. linkplot material wear, link(id) yla(1 2, valuelabel) ysc(r(0.5 2.5)) yla(, ang(h))

. linkplot wear material, link(id) xla(1 2, valuelabel) xsc(r(0.5 2.5)) yla(, ang(h))

Acknowledgments

Vince Wiggins made encouraging noises.

Author

Nicholas J. Cox, Durham University, U.K.

n.j.cox@durham.ac.uk

References

Box, G.E.P., W.G. Hunter and J.S. Hunter. 1978.
Statistics for experimenters: an introduction to design, data analysis, and model building.
New York: John Wiley.

Davison, A.C. 2003. Statistical models.
Cambridge: Cambridge University Press.

Wild, C.J. and G.A.F. Seber. 2000.
Chance encounters: a first course in data analysis and inference.
New York: John Wiley.

Also see

On-line: help for pairplot (if installed)