1. Presentation, data and programs at:
Stata 1, Graphics
Hein Stigum
Presentation, data and programs at:

2. Why use graphs?

3. Problem example Lunch meals per week
Table of means (around 5 per week)
Linear regression
Jan-19
H.S.

4. Problem example 2 Iron level Both linear and logistic regression
Opposite results
Jan-19
H.S.

5. Structure of talk Order Focus: Work/presentation plots Plot types
Outcome type
Focus:
The right plot
The commands
Jan-19
H.S.

6. Plot types

7. Plottypes
Plot at start of analysis: scatter, density, box (raw data)
Plot results: bar, dot (derived data)
Pie: poor, parts of a whole
Main difference: twoway plots individual data, the others plots summaries of data (means, medians, …)
Jan-19
H.S.

8. Continuous outcome

9. Univariate Density Boxplot kdensity weight graph hbox weight Jan-19
H.S.

10. Density with “box” information
Jan-19
H.S.

11. Bivariate Scatter scatter weight gest Jan-19 H.S.
Use scatter and density for
Cont Y and cat x
Cont Y and cont x
Binary Y and cont x
Jan-19
H.S.

12. Scatter and density plots for many types of data
Jan-19
H.S.

13. Twoway density Syntax One plot Two plots overlaid Side by side
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)
Show distributions, density best
Area scales to 1, can compare groups of different size.
Jan-19
H.S.

14. Twoway scatter + fit Syntax Examples
graph twoway (plot1, opts) (plot2, opts), opts
Examples
scatter y x
twoway (scatter y x) (fpfitci y x) (lfit y x)
Fitlines
Scatter shows association and outliers
No kernel smoother with ci
Run 1 ex
lfit
lfitci
Linear
qfit
qfitci
quadratic
mband, mspline
Median band, median spline
fpfitci
Fractional polynomial
lowess
Local regression
Jan-19
H.S.

15. Continuous by 3 categories
Is birth weight the same over parity?
Density plot
Scatterplot
Scatter to see linear/no-linear effect, look for outliers
Density to see equal variance
Equal means? Linear effect?
Outliers?
Equal variances?
Jan-19
H.S.

16. Continuous by 3 categories
Scatterplot
twoway (scatter weight parity3) (fpfitci weight parity3) (lfit weight parity3) , legend(off)
Look for:
Outliers (all analyses)
Non-linear effects (regression)
Scatter to see linear/no-linear effect, look for outliers
Density to see equal variance
Jan-19
H.S.

17. Continuous by 3 categories
Density plot
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)
Scatter to see linear/no-linear effect, look for outliers
Density to see equal variance
Jan-19
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
Jan-19
H.S.

19. Twoway options Syntax Options
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 )
Jan-19
H.S.

20. Continuous by continuous
twoway (scatter weight gest) (fpfitci weight gest) (lfit weight gest)
Look for:
Main effect (line)
Non-linearity (smooth)
outliers
Fpfitci plus lfit gives a graphic test of linearity
Jan-19
H.S.

21. More twoway options Syntax Options
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
Jan-19
H.S.

22. Mark outliers
twoway (scatter weight gest) (scatter weight gest if gest>400, mlabel(id))
Jan-19
H.S.

23. Titles, legend, labels and scale

24. Titles scatter weight gest, title("title") subtitle("subtitle") ///
xtitle("xtitle") ytitle("ytitle") note("note")
Jan-19
H.S.

25. Legend
…, legend( ring(0) pos(11) col(1) label(1 ”Boys, N=283”) label(2 ”Girls, N=270”) )
…, legend(off)
Jan-19
H.S.

26. Axis scale and label scatter weight gest, xscale(range(250 310)) ///
xlabel( 250(20) )
Range will not exclude data, must use “if” for that.
Jan-19
H.S.

27. Categorical outcome

28. Comparing means or proportions
V1, v2, v3 may be cont or binary
X is categorical
Jan-19
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
Could also use scatter y x, connect(direct)
Jan-19
H.S.

30. Binary outcome

31. Scatter: binary by countinuous
Jan-19
H.S.

32. Binary with rug and smooth
gen yy=.
replace yy= 0.02*(lowbw==0) *(lowbw==1)
twoway (rspike yy lowbw gest) (fpfit lowbw gest)
Jan-19
H.S.

33. Regression results Jan-19 H.S. Program not automatic, not show
May use log axis
Jan-19
H.S.