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Please note that the early_int data file (which is used in Chapter 3) is not included among the data files. This was done at the request of the researcher who contributed this data file to ensure the privacy of the participants in the study. Although the web page shows how to obtain the results with this data file, we regret that visitors do not have access to this file to be able to replicate the results for themselves.
Table 3.1, page 48.
use earlyint_pp, clear
list id age cog program in 1/12
id age cog program
1. 68 1 103 1
2. 68 1.5 119 1
3. 68 2 96 1
4. 70 1 106 1
5. 70 1.5 107 1
6. 70 2 96 1
7. 71 1 112 1
8. 71 1.5 86 1
9. 71 2 73 1
10. 72 1 100 1
11. 72 1.5 93 1
12. 72 2 87 1
list id age cog program in 175/186
id age cog program
175. 902 1 119 0
176. 902 1.5 93 0
177. 902 2 99 0
178. 904 1 112 0
179. 904 1.5 98 0
180. 904 2 79 0
181. 906 1 89 0
182. 906 1.5 66 0
183. 906 2 81 0
184. 908 1 117 0
185. 908 1.5 90 0
186. 908 2 76 0
Figure 3.1, page 50.
generate select = inlist(id,68,70,71,72,902,904,906,908) keep if select graph twoway (lfit cog age)(scatter cog age), by(id)
Figure 3.3, page 57.
use earlyint_pp, clear
egen grp=group(id)
generate p1=.
forvalues i = 1/103 {
quietly regress cog age if grp==`i'
quietly predict p
quietly replace p1=p if grp==`i'
quietly drop p
}
graph twoway (scatter p1 age, msym(i) connect(L))(lfit cog age), legend(off)
statsby _b[_cons] _b[time] (e(rmse)^2), by(id): regress cog time
stem _stat_1, round(1)
Stem-and-leaf plot for _stat_1 (_b[_cons])
_stat_1 rounded to integers
_stat_1 rounded to integers
5. | 7
6* |
6. |
7* |
7. | 7
8* | 34
8. | 89
9* | 344
9. | 6666677799
10* | 0012222244
10. | 55666788999
11* | 000111112222333334444
11. | 55677777888999
12* | 12233344
12. | 5556778999
13* | 0013
13. | 55568
14* | 0
stem _stat_2
Stem-and-leaf plot for _stat_2 (_b[time])
-4* | 443111
-3. | 987
-3* | 443322100000
-2. | 9999877776655
-2* | 44322211110000
-1. | 99888877666655
-1* | 4333322211000
-0. | 99998888777765
-0* | 4444332
0* | 134
0. | 79
1* | 0
1. |
2* | 0
stem _stat_3, round(1) lines(1)
Stem-and-leaf plot for _stat_3 (e(rmse)^2)
_stat_3 rounded to integers
0* | 0000111122233334444444466668
1* | 111114447
2* | 044448888
3* | 33338888888
4* | 3338
5* | 44
6* | 777
7* | 44
8* | 111888
9* | 6666
10* | 444
11* | 33
12* | 2
13* | 1
14* |
15* |
16* | 000
17* | 11
18* |
19* | 3
20* |
21* |
22* | 8
23* |
24* | 1
25* | 444
26* | 7
27* |
28* |
29* | 4
30* |
31* |
32* | 3
33* |
34* |
35* |
36* | 8
37* |
38* |
39* |
40* | 00
41* |
42* |
43* |
44* |
45* |
46* | 8
Figure 3.4, page 59.
use earlyint_pp, clear
egen grp=group(id)
generate p1=.
forvalues i = 1/103 {
quietly regress cog age if grp==`i'
quietly predict p
quietly replace p1=p if grp==`i'
quietly drop p
}
graph twoway (scatter p1 age if program==0, msym(i) connect(L))(lfit cog age if program==0), legend(off)
graph twoway (scatter p1 age if program==1, msym(i) connect(L))(lfit cog age if program==1), legend(off)
Table 3.3, page 69.
Note: The xtmixed command is new to Stata 9.
generate prg_time = program*time
xtmixed cog program time prg_time || id: time, variance cov(un) mle
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -1186.0251
[Iterations ommitted ]
Iteration 9: log likelihood = -1184.9703
Computing standard errors:
Mixed-effects ML regression Number of obs = 309
Group variable: id Number of groups = 103
Obs per group: min = 3
avg = 3.0
max = 3
Wald chi2(3) = 242.63
Log likelihood = -1184.9703 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
cog | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
program | 6.854662 2.71105 2.53 0.011 1.541101 12.16822
time | -21.13333 1.883386 -11.22 0.000 -24.8247 -17.44196
prg_time | 5.271264 2.509829 2.10 0.036 .3520907 10.19044
_cons | 107.8407 2.034384 53.01 0.000 103.8534 111.8281
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
id: Unstructured |
var(time) | 10.08915 8.747843 1.844236 55.19411
var(_cons) | 123.9371 25.82037 82.38856 186.4387
cov(time,_cons) | -35.36098 18.02242 -70.68427 -.0376942
-----------------------------+------------------------------------------------
var(Residual) | 74.76618 7.370867 61.62955 90.70295
------------------------------------------------------------------------------
LR test vs. linear regression: chi2(3) = 89.46 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference
estat ic
------------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+----------------------------------------------------------------
| 309 . -1184.97 8 2385.941 2415.807
------------------------------------------------------------------------------
Figure 3.5 on page 71.
predict pred
sort prog age
twoway scatter cog pred age, msymbol(i i) connect(. L) ///
ylabel(50(25)150) xlabel(1(.5)2) legend(off)
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