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Simplifying Looping and Storing with looprun in Stata

Learn how to automate command looping and results storage using looprun in Stata. Examples include profile likelihood functions and Cox regression.

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Simplifying Looping and Storing with looprun in Stata

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  1. Taking the pain out of looping and storing Patrick Royston Nordic and Baltic Stata Users’ meeting, Stockholm,11 November 2011

  2. Overview • I often find myself running a command repeatedly in a loop • I want to save some results and store them in new variable(s) • A new command, looprun, is described that automates the process in a convenient way • It can handle a single loop, or two nested loops • I shall illustrate looprun using profile likelihood functions and surfaces 1

  3. Example 1: Single loop • A non-standard regression in which a non-linear parameter is to be estimated by the profile likelihood method • Vary the parameter over an interval, fit the model • Store the parameter and the resulting deviance (-2 * log likelihood) in new variables • Plot the deviance against the parameter and draw inferences 2

  4. Example 1 • Fitting a Cox regression to a variable haem (haemoglobin) in a kidney cancer dataset • Wish to find the best-fitting power transformation, haemp • Draw inferences about p

  5. Conventional code to solve the problem . capture drop deviance . capture drop p . capture drop order . gen deviance = . . gen p = . . gen int order = _n . local i 0 . quietly foreach p of numlist -3 (0.1) 0.7 { . fracgen haem `p', replace . stcox haem_1 . sort order . local ++i . replace deviance = -2 * e(ll) in `i‘ . replace p = `p' in `i' . } . line deviance p, sort

  6. Solution using looprun . looprun "p=-3(0.1)0.7", generate(deviance) store(-2*e(ll)) : /// fracgen haem @, replace # /// stcox haem_1 . line deviance p, sort

  7. Resulting plot

  8. Example 2: double loop • A non-standard regression in which two non-linear parameters are to be estimated by inspecting the profile likelihood surface • Vary both parameters over a grid, fit the model and store the resulting deviance (-2 * log likelihood) • Plot the deviance against one parameter by the values of the other parameter • Contour plot of the deviance surface • Requires Stata 12 twoway contour 7 7

  9. Example 2 • Model is a Gaussian growth curve • predictor = b1+b2*normal(s*(haem ‒ 12.2) + m/10)

  10. Solution using looprun . looprun "m=7 (2) 35" "s=0.2 (0.05) 2.5", /// generate(deviance, replace) store(-2*e(ll)) : /// capture drop z # /// gen z = normal(@2 * (haem - 12.2) + @1/10) # /// stcox z

  11. Graphs of results Plot deviance against s, by m . sum deviance . gen deviance2 = deviance - r(min) . line deviance2 s, sort by(m)

  12. Resulting “casement” plot

  13. Contour plot . replace deviance2 = min(deviance2, 20) . twoway contour deviance2 m s, ccuts(0(1)20) /// > yscale(r(7 35)) ylabel(10(5)35) xscale(r(.2 2.5)) /// > xlabel(.25(.25)2.5)

  14. Contour plot

  15. What can we learn from the contour plot? • Parameter estimates of m and s are highly correlated • Re-parameterisation might help • The MLE is located along a narrow, long channel • Hence the model may not be well identified in this dataset • The likelihood surface has some peculiarities for low s, high m

  16. Syntax of looprun looprun"[name1=]numlist1" ["[name2=]numlist2"] , required [ options ] :command1 [ # command2 ... ] requireddescription ------------------------------------------------------------------------------------- store(results_list) results to be stored generate(newvarlist [, replace]) names of new variable(s) to store results in optionsdescription ------------------------------------------------------------------------------------- nodots suppresses progress dots nosort do not sort data before storing results separator(string) character separating commands (default #) placeholder(string) placeholder character(s) (default @) -------------------------------------------------------------------------------------

  17. Main limitation: Handling macros • Cannot assign a local or global macro within a looprun subcommand and retrieve it for storage • Easiest way around this is to use scalars, which are global • Need care to avoid clash of scalar names with similarly named variables

  18. Conclusion • looprun should take most of the effort out of many simple programming tasks in Stata • looprun can be installed via my UCL webpage: net from http://www.homepages.ucl.ac.uk/~ucakjpr/stata/

  19. Thank you. 18

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