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

11 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

2 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

Example 1 Fitting a Cox regression to a variable haem (haemoglobin) in a kidney cancer dataset Wish to find the best-fitting power transformation, haem p Draw inferences about p 3

4 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

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

6 Resulting plot

77 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

Example 2 Model is a Gaussian growth curve predictor = b 1 +b 2 *normal(s*( haem ‒ 12.2) + m/10) 8

9 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 = * (haem ) # /// stcox z

Graphs of results 10 Plot deviance against s, by m. sum deviance. gen deviance2 = deviance - r(min). line deviance2 s, sort by(m)

11 Resulting “casement” plot

Contour plot 12. 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)

13 Contour plot

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 14

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

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 16

17 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

18 Thank you.