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Stata 1, Graphics

Stata 1, Graphics. Hein Stigum Presentation, data and programs at: http://folk.uio.no/heins/. Why use graphs?. Problem example. Lunch meals per week Table of means (around 5 per week) Linear regression. Problem example 2. Iron level Both linear and logistic regression Opposite results.

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Stata 1, Graphics

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  1. Stata 1, Graphics Hein Stigum Presentation, data and programs at: http://folk.uio.no/heins/

  2. Why use graphs?

  3. Problem example • Lunch meals per week • Table of means (around 5 per week) • Linear regression H.S.

  4. Problem example 2 • Iron level • Both linear and logistic regression • Opposite results H.S.

  5. Structure of talk • Order • Work/presentation plots • Plot types • Outcome type • Focus: • The right plot • The commands H.S.

  6. Plot types

  7. Plottypes H.S.

  8. Continuous outcome

  9. Density kdensity weight Boxplot graph hbox weight Univariate H.S.

  10. Density with “box” information H.S.

  11. Bivariate • Scatter • scatter weight gest H.S.

  12. Scatter and density plots for many types of data H.S.

  13. Twoway density • Syntax • graph twoway (plot1, opts) (plot2, opts), opts • One plot • kdensity x • Two plots overlaid • twoway ( kdensity weight if sex==1, lcolor(blue) ) /// ( kdensity weight if sex==2, lcolor(red) ) • Side by side • twoway ( kdensity weight ), by(sex) H.S.

  14. Twoway scatter + fit • Syntax • graph twoway (plot1, opts) (plot2, opts), opts • Examples • scatter y x • twoway (scatter y x) (fpfitci y x) (lfit y x) Fitlines H.S.

  15. Continuous by 3 categories • Is birth weight the same over parity? Density plot Scatterplot Equal means? Linear effect? Outliers? Equal variances? H.S.

  16. twoway (scatter weight parity3) (fpfitci weight parity3) (lfit weight parity3) , legend(off) Look for: Outliers (all analyses) Non-linear effects (regression) Continuous by 3 categories Scatterplot H.S.

  17. Continuous by 3 categories twoway (kdensity weight if parity3==0, lcol(black)) (kdensity weight if parity3==1, lcol(blue)) (kdensity weight if parity3==2, lcol(red)) , yscale(off) • Look for: • Different locations • Different shapes (ANOVA, regression) Density plot H.S.

  18. Twoway density options • kdensity x, normal add normal curve • kdensity x, area(400) frequency, N=400 • display r(width) previous width • kdensity x, width(80) less smoothing H.S.

  19. Twoway options • Syntax • graph twoway (plot1, opts) (plot2, opts), opts • Options • lcolor(red) line color • lpattern(“.-”) line pattern • lwidth(*2) line width *2 • legend( ring(0) legend inside plot pos(2) legend at 2 o’clock position col(1) legends in 1 column label(1 “First”) legend label plot 1 label(2 “Second”) legend label plot 2 ) H.S.

  20. twoway (scatter weight gest) (fpfitci weight gest) (lfit weight gest) Look for: Main effect (line) Non-linearity (smooth) outliers Continuous by continuous H.S.

  21. More twoway options • Syntax • graph twoway (plot1, opts) (plot2, opts), opts • Options • msize(*0.5) marker size • mlabel(id) marker label =variable id • xline(24) line at x=24 • scale(1.5) all elements 1.5*larger H.S.

  22. Mark outliers twoway (scatter weight gest) (scatter weight gest if gest>400, mlabel(id)) H.S.

  23. Titles, legend, labels and scale

  24. Titles scatter weight gest, title("title") subtitle("subtitle") /// xtitle("xtitle") ytitle("ytitle") note("note") H.S.

  25. …, legend( ring(0) pos(11) col(1) label(1 ”Boys, N=283”) label(2 ”Girls, N=270”) ) …, legend(off) Legend H.S.

  26. Axis scale and label scatter weight gest, xscale(range(250 310)) /// xlabel( 250(20)310 281) H.S.

  27. Categorical outcome

  28. Comparing means or proportions H.S.

  29. Comparing means/prop. better preserve “save” data collapse (mean) v1 v2 v3, by(parity) aggregate list list the new data twoway (scatter v1 parity) (line v1 parity) /// (scatter v2 parity) (line v2 parity) /// (scatter v3 parity) (line v3 parity) restore restore original data H.S.

  30. Binary outcome

  31. Scatter: binary by countinuous H.S.

  32. gen yy=. replace yy= 0.02*(lowbw==0)+ 0.98*(lowbw==1) twoway (rspike yy lowbw gest) (fpfit lowbw gest) Binary with rug and smooth H.S.

  33. Regression results H.S.

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