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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: -linkplot- available on SSC |

Date |
Wed, 9 Jul 2003 15:47:10 +0100 |

Thanks to Kit Baum, a new package -linkplot- has been added to SSC. This requires Stata 8. To install, type . ssc inst linkplot in an up-to-date net-aware version of Stata 8. -linkplot- draws linked (i.e. connected) scatter plots. How does this differ from what is already available through -connect()- options? Nothing in principle, but a bit more than that in practice. Let's dive straight into an example. 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). The units are not specified. 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 Broadly speaking, variations within boys (same boy, different shoes) are less than variations between boys, but of more interest. (The design assigns materials randomly to left and right feet, to avoid "left shoeness" or "right shoeness", etc.) Graphically, therefore, we need ways of showing the data that let us appreciate the fine structure. Some possibilities are provided by -pairplot- on SSC, and -linkplot- provides others. 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, and elsewhere with paired data, likely to be 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. 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) id material wear 1. 1 A 13.2 2. 1 B 14 3. 2 A 8.2 4. 2 B 8.8 5. 3 A 10.9 6. 3 B 11.2 7. 4 A 14.3 8. 4 B 14.2 9. 5 A 10.7 10. 5 B 11.8 11. 6 A 6.6 12. 6 B 6.4 13. 7 A 9.5 14. 7 B 9.8 15. 8 A 10.8 16. 8 B 11.3 17. 9 A 8.8 18. 9 B 9.3 19. 10 A 13.3 20. 10 B 13.6 Now we can plot -wear- and -material- on different axes. (-material- was produced by -encode-, so is numeric underneath its value labels.) But with this data structure, any connections will typically not be all vertical or all horizontal. As it happens, you can use -connect()- for virtually any kind of connection, so long as the data have been put in the right sort order, and (for some problems) missing values have been inserted, which you do not want to connect over, but that's a fairly awkward "so long as", which is why -linkplot- codifies the nitty-gritty. Some possibilities are . 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)) The general idea is that you need to specify a -link()- variable defining groups to be linked. Usually this will be some sort of identifier variable, so the idea has panel data applications. Some of the tricks for getting data in the right sort order are discussed in rather dusty old FAQs at http://www.stata.com/support/faqs/graphics/connect.html http://www.stata.com/support/faqs/graphics/vplplot.html although Stata 8 adds a nicer way to do it all, through -cmissing()-, which is in fact the main trick within -linkplot-. More technicalities are covered in the help file. Vince Wiggins provided encouraging noises as I worked my way towards this. 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. Wild, C.J. and G.A.F. Seber. 2000. Chance encounters: a first course in data analysis and inference. New York: John Wiley. Nick n.j.cox@durham.ac.uk * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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