1. Mar-16H.S.1 Error check in data Hein Stigum Presentation, data and programs at: http://folk.uio.no/heins/

2. Example data HUMIS –Birth cohort, 5 counties in Norway –N=475 mother-child pairs –Repeated questionnaires Purpose –Outcome:Growth after birth –Exposure:Contaminants in mother’s milk Mar-16H.S.2

3. Mar-16H.S.3 Agenda Potential problems –String variables, Missing, … Univariate Bivariate Multivariable Individual growth

4. Mar-16H.S.4 Potential problems

5. Mar-16H.S.5 String variables encode KJONN if KJONN!=" ", generate(sex3) String to numeric

6. Mar-16H.S.6 Missing

7. Mar-16H.S.7 Univariate outliers

8. Mar-16H.S.8 Commands for previous plot local i=1 foreach var of varlist age1 weight1 fHCB BMI1 mHeight mWeight { graph hbox `var', marker(1, mlabel(id) msymbol(i) mlabpos(0) mlabangle(-90)) /// name(plt`i', replace) local ++i } graph combine plt1 plt2 plt3 plt4 plt5 plt6, col(2)

9. Mar-16H.S.9 Bivariate outliers

10. Mar-16H.S.10 Commands for previous plot twoway (scatter mWeight mHeight) /// (scatter mWeight mHeight if BMI1>35 | BMI1<16, mcol(red))/// (qfit mWeight mHeight)/// (qfit mWeight mHeight if mHeight<185)///, legend(off) text(110 195 "BMI>35", col(red)) /// ytitle("Mother's weight") xtitle("Mother's height")

11. Mar-16H.S.11 Multivariable outliers Weight

12. Mar-16H.S.12 Commands for previous plot gen agesq=age^2 gen ageqb=age^3 regress weight age agesq ageqb if age>=0 & age<1000 capture: drop xb res predict xb, xb/* predicted value */ predict res, res/* residuals */ tw (scatter weight age)(scatter weight age if abs(res)>4000, mcol(red))/// (line xb age, sort lcol(red)) if age>=0 & age<1000, legend(off)

13. Mar-16H.S.13 Plot of individual growth patterns: weight versus age

14. Mar-16H.S.14 Weight by age 1

15. Mar-16H.S.15 Weight by age 2

16. Mar-16H.S.16 Weight by age 3

17. Mar-16H.S.17 Weight by age 4

18. Mar-16H.S.18 Weight by age 5

19. Mar-16H.S.19 Weight by age 6

20. Mar-16H.S.20 Weight by age 7

21. Mar-16H.S.21 Weight by age 8

22. Mar-16H.S.22 Weight by age 9

23. Mar-16H.S.23 Weight by age 10

24. Mar-16H.S.24 Weight by age 11

25. Mar-16H.S.25 Weight by age 12

26. Mar-16H.S.26 Weight by age 13

27. Mar-16H.S.27 Weight by age 14

28. Mar-16H.S.28 Weight by age 15

29. Mar-16H.S.29 Weight by age 16

30. Commands for previous plots * Individual growth patterns. OBS 16 pages of each 30 plots * Repeated measurements, long format, age nested in id sort id age/* sort by id-number and age */ global d=30/* 30 plots per page */ forvalues i=1(1)16 {/* 16 pages*30 plots=480 subjects */ local j=(`i'-1)*$d+1/* plot subjects in id-interval: j<=id<=k */ local k=`i'*$d twoway (line weight age, connect(ascending)) if id>=`j' & id<=`k‘ ///,by(id, compact title("Weight by age, `i'") note("") ) /// ylabel(0(5000)15000) xlabel(0(200)800) graph export “H:\Projects\HUMIS\Weight gain\plt`i'.emf", replace /* Enhanced Metafile Format */ }/* end of loop */ * Make new Photo album in Powerpoint, and add all plots. This will give one plot per page in max size. Mar-16H.S.30

31. Mar-16H.S.31 After new data merge Plot of individual growth patterns: weight versus age

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48. Mar-16H.S.48 Individual plots in large datasets? Scan 1 page (=30 curves) in 5 sec –Hours used=5N/(30*60*60) Scan all –If N=50 000, need 2.3 hours May instead scan curves of subjects with medium to large residuals. –Residual>1000 finds 190 of the 470 children=40% 12 of the 15 deviant growth patterns=80%

49. Summing up Graph, outliers –Uni:Boxplots –Bi:Scatterplots –Multi:Scatterplots+residuals –Individual growth Merge errors are not rare! Mar-16H.S.49