Figure 2.1, page 18.
use https://stats.idre.ucla.edu/stat/stata/examples/alda/data/tolerance, clear
list
id tol11 tol12 tol13 tol14 tol15 male exposure
1. 9 2.23 1.79 1.9 2.12 2.66 0 1.54
2. 45 1.12 1.45 1.45 1.45 1.99 1 1.16
3. 268 1.45 1.34 1.99 1.79 1.34 1 .9
4. 314 1.22 1.22 1.55 1.12 1.12 0 .81
5. 442 1.45 1.99 1.45 1.67 1.9 0 1.13
6. 514 1.34 1.67 2.23 2.12 2.44 1 .9
7. 569 1.79 1.9 1.9 1.99 1.99 0 1.99
8. 624 1.12 1.12 1.22 1.12 1.22 1 .98
9. 723 1.22 1.34 1.12 1 1.12 0 .81
10. 918 1 1 1.22 1.99 1.22 0 1.21
11. 949 1.99 1.55 1.12 1.45 1.55 1 .93
12. 978 1.22 1.34 2.12 3.46 3.32 1 1.59
13. 1105 1.34 1.9 1.99 1.9 2.12 1 1.38
14. 1542 1.22 1.22 1.99 1.79 2.12 0 1.44
15. 1552 1 1.12 2.23 1.55 1.55 0 1.04
16. 1653 1.11 1.11 1.34 1.55 2.12 0 1.25
reshape long tol, i(id) j(age)
(note: j = 11 12 13 14 15)
Data wide -> long
-----------------------------------------------------------------------------
Number of obs. 16 -> 80
Number of variables 8 -> 5
j variable (5 values) -> age
xij variables:
tol11 tol12 ... tol15 -> tol
-----------------------------------------------------------------------------
list
id age tol male exposure
1. 9 11 2.23 0 1.54
2. 9 12 1.79 0 1.54
3. 9 13 1.9 0 1.54
4. 9 14 2.12 0 1.54
5. 9 15 2.66 0 1.54
6. 45 11 1.12 1 1.16
7. 45 12 1.45 1 1.16
8. 45 13 1.45 1 1.16
9. 45 14 1.45 1 1.16
.. output omitted ...
76. 1653 11 1.11 0 1.25
77. 1653 12 1.11 0 1.25
78. 1653 13 1.34 0 1.25
79. 1653 14 1.55 0 1.25
80. 1653 15 2.12 0 1.25
Table 2.1, page 20.
To get back to the “person-level” data set we can type reshape wide without any arguments or options.
reshape wide
(note: j = 11 12 13 14 15)
Data long -> wide
-----------------------------------------------------------------------------
Number of obs. 80 -> 16
Number of variables 5 -> 8
j variable (5 values) age -> (dropped)
xij variables:
tol -> tol11 tol12 ... tol15
-----------------------------------------------------------------------------
correlate tol11 tol12 tol13 tol14 tol15
(obs=16)
| tol11 tol12 tol13 tol14 tol15
-------------+---------------------------------------------
tol11 | 1.0000
tol12 | 0.6573 1.0000
tol13 | 0.0619 0.2476 1.0000
tol14 | 0.1408 0.2056 0.5872 1.0000
tol15 | 0.2635 0.3923 0.5692 0.8255 1.0000
Figure 2.2, page 25.
We return to “person-period” data set by type reshape long without arguments or options.
reshape long graph twoway scatter tol age, by(id) ylabel(0(1)4) xlabel(11(1)15)
Figure 2.3, page 27.
use https://stats.idre.ucla.edu/stat/stata/examples/alda/data/tolerance_pp, clear graph twoway (lowess tolerance age)(scatter tolerance age), by(id)
Table 2.2, page 30.
use https://stats.idre.ucla.edu/stat/stata/examples/alda/data/tolerance_pp, clear
statsby _b[_cons] _se[_cons] _b[time] _se[time] (e(rmse)^2) e(r2), by(id) saving(statby): regress tolerance time
(running regress on estimation sample)
command: regress tolerance time
_stat_1: _b[_cons]
_stat_2: _se[_cons]
_stat_3: _b[time]
_stat_4: _se[time]
_stat_5: e(rmse)^2
_stat_6: e(r2)
by: id
Statsby groups
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
...............
use statby
list, clean
id _stat_1 _stat_2 _stat_3 _stat_4 _stat_5 _stat_6
1. 9 1.902 .2519484 .119 .1028575 .1057967 .3085185
2. 45 1.144 .1333567 .174 .0544426 .02964 .7729779
3. 268 1.536 .2603805 .023 .1062999 .1129967 .0153654
4. 314 1.306 .1526565 -.03 .0623217 .03884 .0717018
5. 442 1.576 .2078653 .058 .0848607 .0720133 .1347324
6. 514 1.43 .1379493 .265 .0563176 .0317167 .8806747
7. 569 1.816 .0257294 .049 .010504 .0011033 .8788434
8. 624 1.12 .04 .02 .0163299 .0026667 .3333333
9. 723 1.268 .0844275 -.054 .0344674 .01188 .45
10. 918 1 .3044437 .143 .1242886 .1544767 .3061594
11. 949 1.728 .2411804 -.098 .0984615 .0969467 .2482424
12. 978 1.028 .31995 .632 .130619 .1706134 .8864112
13. 1105 1.538 .1511555 .156 .061709 .03808 .6805369
14. 1542 1.194 .1803275 .237 .0736184 .0541967 .775515
15. 1552 1.184 .3735532 .153 .1525025 .23257 .2512234
16. 1653 .9540001 .1392551 .246 .0568507 .03232 .861904
Figure 2.4, page 31.
Note: Use the data that remain in memory after issuing the statsby command in table 2.2.
stem _stat_1, round(.01) /* _stat_1 is fitted initial status */ 9* | 5 10* | 03 11* | 2489 12* | 7 13* | 1 14* | 3 15* | 448 16* | 17* | 3 18* | 2 19* | 0 stem _stat_3, round(.01) /* _stat_3 is fitted rate of change */ -1* | 0 -0* | 53 0* | 2256 1* | 24567 2* | 456 3* | 4* | 5* | 6* | 3 stem _stat_5, round(.01) /* _stat_5 is residual variance */ 0* | 00133344 0. | 57 1* | 011 1. | 57 2* | 3 stem _stat_6, round(.01) /* _stat_6 is r-squared */ 0* | 27 1* | 3 2* | 55 3* | 113 4* | 5 5* | 6* | 8 7* | 78 8* | 6889 save statby, replace
Figure 2.5, page 32.
use https://stats.idre.ucla.edu/stat/stata/examples/alda/data/tolerance_pp, clear graph twoway (lfit tolerance age)(scatter tolerance age), by(id)
Table 2.3, page 37.
Note: Use the data that remain in memory after issuing the statsby command in table 2.2.
use statby, clear
summarize _stat_1 _stat_3
Variable | Obs Mean Std. Dev. Min Max
-------------+-----------------------------------------------------
_stat_1 | 16 1.35775 .2977792 .9540001 1.902
_stat_3 | 16 .1308125 .1722959 -.098 .632
correlate _stat_1 _stat_3
(obs=16)
| _stat_1 _stat_3
-------------+---------------------
_stat_1 | 1.0000
_stat_3 | -0.4481 1.0000
Figure 2.7, page 38.
/* panel 2 */
use https://stats.idre.ucla.edu/stat/stata/examples/alda/data/tolerance_pp, clear
egen grp=group(id)
generate p1=.
forvalues i = 1/16 {
quietly regress tolerance age if grp==`i'
quietly predict p
quietly replace p1=p if grp==`i'
quietly drop p
}
/* panel 1 (labels reversed in book) */
graph twoway (scatter p1 age if ~male, msym(i) connect(L))(lfit tolerance age, lwidth(thick))
/* panel 2 (labels reversed in book) */
graph twoway (scatter p1 age if male, msym(i) connect(L))(lfit tolerance age, lwidth(thick))
generate hiexp= exposure>1.145
/* panel 3 */
graph twoway (scatter p1 age if ~hiexp, msym(i) connect(L))(lfit tolerance age if ~hiexp, lwidth(thick))
/* panel 4 */
graph twoway (scatter p1 age if hiexp, msym(i) connect(L))(lfit tolerance age if hiexp, lwidth(thick))
Figure 2.8, page 40.
use https://stats.idre.ucla.edu/stat/stata/examples/alda/data/tolerance, clear
sort id
save tolerance, replace
use https://stats.idre.ucla.edu/stat/stata/examples/alda/data/tolerance_pp, clear
statsby _b[_cons] _se[_cons] _b[time] _se[time] (e(rmse)^2) e(r2), by(id) saving(statby, replace): ///
regress tolerance time
use statby, clear
rename _stat_1 initialfitted
rename _stat_2 initialse
rename _stat_3 changeratefitted
rename _stat_4 changeratese
rename _stat_5 residvariance
rename _stat_6 rsquared
merge id using tolerance
drop _merge
correlate initialfitted male
local rho1=round(r(rho), .01)
graph twoway scatter initialfitted male, xlabel(0 1) ylabel(.5 1.5 2.5, nogrid) ///
title("Fitted Initial Status", position(11)) text(.7 1.5 "r = `rho1'") ytitle(" ") ///
xscale(r(-1 2)) name(g1, replace)
correlate initialfitted exposure
local rho2=round(r(rho), .01)
graph twoway scatter initialfitted exposure, xlabel(1 2) ylabel(.5 1.5 2.5, nogrid) ///
title("Fitted Initial Status", position(11)) text(.7 2.5 "r = `rho2'") ytitle(" ") ///
xscale(r(0 3)) name(g2, replace)
correlate changeratefitted male
local rho3=round(r(rho), .01)
graph twoway scatter changeratefitted male, xlabel(0 1) ylabel(0 .4 .8, nogrid) ///
title("Fitted Rate of Change", position(11)) text(0 1.5 "r = `rho3'") ytitle(" ") ///
xscale(r(-1 2)) name(g3, replace)
correlate changeratefitted exposure
local rho4=round(r(rho), .01)
graph twoway scatter changeratefitted exposure, xlabel(1 2) ylabel(0 .4 .8, nogrid) ///
title("Fitted Rate of Change", position(11)) ytitle(" ") text(0 2.5 "r = `rho4'") ///
xscale(r(0 3)) name(g4, replace)
graph combine g1 g2 g3 g4
