|
|
|
||||
|
|
|||||
Figure 7.1, page 218.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/toxic, clear
regress close lived
Source | SS df MS Number of obs = 153
---------+------------------------------ F( 1, 151) = 13.65
Model | 3.11094265 1 3.11094265 Prob > F = 0.0003
Residual | 34.4184691 151 .227936882 R-squared = 0.0829
---------+------------------------------ Adj R-squared = 0.0768
Total | 37.5294118 152 .246904025 Root MSE = .47743
------------------------------------------------------------------------------
close | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------+--------------------------------------------------------------------
lived | -.0084379 .002284 -3.694 0.000 -.0129506 -.0039252
_cons | .593954 .0585363 10.147 0.000 .4782981 .7096099
------------------------------------------------------------------------------
graph twoway (scatter close lived if close == 1, connect(l)) ///
(scatter close lived if close == 0, connect(l)) ///
(lfit close lived), xlabel(0(10)80) ylabel(0(.2)1)
Figure 7.2, page 219.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/toxic, clear graph hbox lived, over(close)
Figure 7.4, page 222.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/toxic, clear
logit close lived
Iteration 0: log likelihood = -104.60578
Iteration 1: log likelihood = -97.80942
Iteration 2: log likelihood = -97.634236
Iteration 3: log likelihood = -97.633571
Logit estimates Number of obs = 153
LR chi2(1) = 13.94
Prob > chi2 = 0.0002
Log likelihood = -97.633571 Pseudo R2 = 0.0667
------------------------------------------------------------------------------
close | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
lived | -.0409876 .0121398 -3.376 0.001 -.0647811 -.0171941
_cons | .4599786 .2625643 1.752 0.080 -.054638 .9745953
------------------------------------------------------------------------------
predict yhat1
graph twoway (scatter close lived if close == 1, connect(l)) ///
(scatter close lived if close == 0, connect(l)) ///
(line yhat1 lived, sort) (lfit close lived), xlabel(0(10)80) ylabel(0(.2)1)
Table 224, page 224.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/toxic, clear
logit close lived
Iteration 0: log likelihood = -104.60578
Iteration 1: log likelihood = -97.80942
Iteration 2: log likelihood = -97.634236
Iteration 3: log likelihood = -97.633571
Logit estimates Number of obs = 153
LR chi2(1) = 13.94
Prob > chi2 = 0.0002
Log likelihood = -97.633571 Pseudo R2 = 0.0667
------------------------------------------------------------------------------
close | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
lived | -.0409876 .0121398 -3.376 0.001 -.0647811 -.0171941
_cons | .4599786 .2625643 1.752 0.080 -.054638 .9745953
------------------------------------------------------------------------------
Table 7.2, page 226.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/toxic, clear
logit close lived educ contam hsc
Iteration 0: log likelihood = -104.60578
Iteration 1: log likelihood = -76.104878
Iteration 2: log likelihood = -74.725772
Iteration 3: log likelihood = -74.690849
Iteration 4: log likelihood = -74.690816
Logit estimates Number of obs = 153
LR chi2(4) = 59.83
Prob > chi2 = 0.0000
Log likelihood = -74.690816 Pseudo R2 = 0.2860
------------------------------------------------------------------------------
close | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
lived | -.0464826 .0149263 -3.114 0.002 -.0757376 -.0172276
educ | -.1659221 .0899316 -1.845 0.065 -.3421849 .0103407
contam | 1.208137 .4653958 2.596 0.009 .2959783 2.120296
hsc | 2.17289 .4641192 4.682 0.000 1.263233 3.082547
_cons | 1.731439 1.301999 1.330 0.184 -.8204311 4.28331
------------------------------------------------------------------------------
Table 7.3, page 227.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/toxic, clear
logit close lived educ contam hsc female kids nodad
Iteration 0: log likelihood = -104.60578
Iteration 1: log likelihood = -73.307756
Iteration 2: log likelihood = -70.718684
Iteration 3: log likelihood = -70.526461
Iteration 4: log likelihood = -70.52469
Iteration 5: log likelihood = -70.524689
Logit estimates Number of obs = 153
LR chi2(7) = 68.16
Prob > chi2 = 0.0000
Log likelihood = -70.524689 Pseudo R2 = 0.3258
------------------------------------------------------------------------------
close | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
lived | -.0466422 .0169751 -2.748 0.006 -.0799129 -.0133716
educ | -.2060233 .093197 -2.211 0.027 -.3886861 -.0233605
contam | 1.282082 .4813682 2.663 0.008 .3386177 2.225546
hsc | 2.418002 .5096638 4.744 0.000 1.419079 3.416924
female | -.0515618 .5571215 -0.093 0.926 -1.1435 1.040376
kids | -.6706227 .5656146 -1.186 0.236 -1.779207 .4379616
nodad | -2.225988 .9991178 -2.228 0.026 -4.184223 -.2677527
_cons | 2.893725 1.602985 1.805 0.071 -.2480675 6.035517
------------------------------------------------------------------------------
Table 7.4, page 228.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/toxic, clear
logit close lived educ contam hsc nodad
Iteration 0: log likelihood = -104.60578
Iteration 1: log likelihood = -73.813367
Iteration 2: log likelihood = -71.47445
Iteration 3: log likelihood = -71.327206
Iteration 4: log likelihood = -71.326227
Iteration 5: log likelihood = -71.326227
Logit estimates Number of obs = 153
LR chi2(5) = 66.56
Prob > chi2 = 0.0000
Log likelihood = -71.326227 Pseudo R2 = 0.3181
------------------------------------------------------------------------------
close | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
lived | -.0396488 .0154812 -2.561 0.010 -.0699914 -.0093062
educ | -.1966667 .0926128 -2.124 0.034 -.3781846 -.0151489
contam | 1.298551 .4766294 2.724 0.006 .3643749 2.232728
hsc | 2.27855 .4903703 4.647 0.000 1.317441 3.239658
nodad | -1.730948 .7252746 -2.387 0.017 -3.15246 -.309436
_cons | 2.182273 1.330141 1.641 0.101 -.4247561 4.789301
------------------------------------------------------------------------------
Figure 7.5, page 232.Note that the equations for these curves are listed and explained on page 231.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/toxic, clear
logit close lived educ contam hsc nodad
Iteration 0: log likelihood = -104.60578
Iteration 1: log likelihood = -73.813367
Iteration 2: log likelihood = -71.47445
Iteration 3: log likelihood = -71.327206
Iteration 4: log likelihood = -71.326227
Iteration 5: log likelihood = -71.326227
Logit estimates Number of obs = 153
LR chi2(5) = 66.56
Prob > chi2 = 0.0000
Log likelihood = -71.326227 Pseudo R2 = 0.3181
------------------------------------------------------------------------------
close | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
lived | -.0396488 .0154812 -2.561 0.010 -.0699914 -.0093062
educ | -.1966667 .0926128 -2.124 0.034 -.3781846 -.0151489
contam | 1.298551 .4766294 2.724 0.006 .3643749 2.232728
hsc | 2.27855 .4903703 4.647 0.000 1.317441 3.239658
nodad | -1.730948 .7252746 -2.387 0.017 -3.15246 -.309436
_cons | 2.182273 1.330141 1.641 0.101 -.4247561 4.789301
------------------------------------------------------------------------------
To obtain the predicted probabilities for a subject's profile, we are going to use the postgr3 command and use the x( ) option to define the profile that we are interested. You can download postgr3 from within Stata by typing findit postgr3 (see How can I use the findit command to search for programs and get additional help? for more information about using findit).
postgr3 lived, gen(avg) nodraw postgr3 lived, gen(bottom) x(contam=0 hsc=0 nodad=1) nodraw postgr3 lived, gen(top) x(contam=1 hsc=1 nodad=0) nodraw graph twoway (line avg lived, sort) (line bottom lived, sort) (line top lived, sort), /// xlabel(0(10)80)
Figure 7.6, page 232.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/toxic, clear
logit close lived educ contam hsc nodad
Iteration 0: log likelihood = -104.60578
Iteration 1: log likelihood = -73.813367
Iteration 2: log likelihood = -71.47445
Iteration 3: log likelihood = -71.327206
Iteration 4: log likelihood = -71.326227
Iteration 5: log likelihood = -71.326227
Logit estimates Number of obs = 153
LR chi2(5) = 66.56
Prob > chi2 = 0.0000
Log likelihood = -71.326227 Pseudo R2 = 0.3181
------------------------------------------------------------------------------
close | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
lived | -.0396488 .0154812 -2.561 0.010 -.0699914 -.0093062
educ | -.1966667 .0926128 -2.124 0.034 -.3781846 -.0151489
contam | 1.298551 .4766294 2.724 0.006 .3643749 2.232728
hsc | 2.27855 .4903703 4.647 0.000 1.317441 3.239658
nodad | -1.730948 .7252746 -2.387 0.017 -3.15246 -.309436
_cons | 2.182273 1.330141 1.641 0.101 -.4247561 4.789301
------------------------------------------------------------------------------
summ lived educ contam hsc nodad
Variable | Obs Mean Std. Dev. Min Max
---------+-----------------------------------------------------
lived | 153 19.26797 16.95466 1 81
educ | 153 12.95425 2.431536 6 20
contam | 153 .2810458 .450986 0 1
hsc | 153 .3071895 .4628437 0 1
nodad | 153 .1699346 .3768088 0 1
postgr3 contam, gen(avg1) nodraw postgr3 contam, gen(top1) x(lived=min educ=min hsc=1 nodad=0) nodraw postgr3 contam, gen(bottom1) x(lived=max educ=max hsc=0 nodad=1) nodraw graph twoway (line avg1 contam, sort) (line bottom1 contam, sort) (line top1 contam, sort), /// xlabel(0 1)
Table 7.5, page 234.
NOTE: Hamilton gives the data set at the top of the page. You need to input it before you can run the logit.
clear input less mother cnt 0 0 202 0 1 79 1 0 44 1 1 0 end logit less mother [fw=cnt] Note: mother~=0 predicts failure perfectly mother dropped and 1 obs not used Iteration 0: log likelihood = -115.53714 Logit estimates Number of obs = 246 LR chi2(0) = 0.00 Prob > chi2 = . Log likelihood = -115.53714 Pseudo R2 = 0.0000 ------------------------------------------------------------------------------ less | Coef. Std. Err. z P>|z| [95% Conf. Interval] ---------+-------------------------------------------------------------------- _cons | -1.524078 .1663664 -9.161 0.000 -1.85015 -1.198006 ------------------------------------------------------------------------------
Figure 7.7, page 239.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/toxic, clear
logistic close lived educ contam hsc nodad
Logit estimates Number of obs = 153
LR chi2(5) = 66.56
Prob > chi2 = 0.0000
Log likelihood = -71.326227 Pseudo R2 = 0.3181
------------------------------------------------------------------------------
close | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
lived | .9611269 .0148794 -2.561 0.010 .9324019 .9907369
educ | .8214643 .0760781 -2.124 0.034 .6851041 .9849652
contam | 3.663985 1.746363 2.724 0.006 1.439614 9.325267
hsc | 9.76251 4.787244 4.647 0.000 3.733856 25.52498
nodad | .1771164 .128458 -2.387 0.017 .0427468 .7338607
------------------------------------------------------------------------------
predict x, dx2
predict lhat
graph twoway (scatter x lhat), xlabel(0(.2)1) ylabel(0(5)30)
Figure 7.8, page 240.
predict z, ddeviance graph twoway scatter z lhat, xlabel(0(.2)1) ylabel(0(1)7)
graph twoway scatter b lhat, xlabel(0(.2)1) ylabel(0(.1).7)
Table 7.8, page 241.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/toxic, clear
logit model estimated with full sample (n=153)
logit close lived educ contam hsc nodad
Iteration 0: log likelihood = -104.60578
Iteration 1: log likelihood = -73.813367
Iteration 2: log likelihood = -71.47445
Iteration 3: log likelihood = -71.327206
Iteration 4: log likelihood = -71.326227
Iteration 5: log likelihood = -71.326227
Logit estimates Number of obs = 153
LR chi2(5) = 66.56
Prob > chi2 = 0.0000
Log likelihood = -71.326227 Pseudo R2 = 0.3181
------------------------------------------------------------------------------
close | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
lived | -.0396488 .0154812 -2.561 0.010 -.0699914 -.0093062
educ | -.1966667 .0926128 -2.124 0.034 -.3781846 -.0151489
contam | 1.298551 .4766294 2.724 0.006 .3643749 2.232728
hsc | 2.27855 .4903703 4.647 0.000 1.317441 3.239658
nodad | -1.730948 .7252746 -2.387 0.017 -3.15246 -.309436
_cons | 2.182273 1.330141 1.641 0.101 -.4247561 4.789301
------------------------------------------------------------------------------
with X pattern 131 deleted (n=152)
use http://www.ats.ucla.edu/stat/stata/examples/rwg/toxic, clear
logistic close lived educ contam hsc nodad
Logit estimates Number of obs = 153
LR chi2(5) = 66.56
Prob > chi2 = 0.0000
Log likelihood = -71.326227 Pseudo R2 = 0.3181
------------------------------------------------------------------------------
close | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
lived | .9611269 .0148794 -2.561 0.010 .9324019 .9907369
educ | .8214643 .0760781 -2.124 0.034 .6851041 .9849652
contam | 3.663985 1.746363 2.724 0.006 1.439614 9.325267
hsc | 9.76251 4.787244 4.647 0.000 3.733856 25.52498
nodad | .1771164 .128458 -2.387 0.017 .0427468 .7338607
------------------------------------------------------------------------------
predict b, dbeta
predict y, number
drop if y==131
(1 observation deleted)
logit close lived educ contam hsc nodad
Iteration 0: log likelihood = -103.76066
Iteration 1: log likelihood = -71.127431
Iteration 2: log likelihood = -68.285763
Iteration 3: log likelihood = -68.06416
Iteration 4: log likelihood = -68.061885
Iteration 5: log likelihood = -68.061885
Logit estimates Number of obs = 152
LR chi2(5) = 71.40
Prob > chi2 = 0.0000
Log likelihood = -68.061885 Pseudo R2 = 0.3440
------------------------------------------------------------------------------
close | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
lived | -.0524225 .0168844 -3.105 0.002 -.0855153 -.0193297
educ | -.2140323 .0951634 -2.249 0.025 -.4005492 -.0275155
contam | 1.381912 .4896501 2.822 0.005 .4222155 2.341609
hsc | 2.346783 .5030237 4.665 0.000 1.360875 3.332691
nodad | -1.658292 .7484702 -2.216 0.027 -3.125267 -.1913172
_cons | 2.52996 1.370969 1.845 0.065 -.1570907 5.217011
------------------------------------------------------------------------------
with X pattern 3 deleted (n=152)
use http://www.ats.ucla.edu/stat/stata/examples/rwg/toxic, clear
logistic close lived educ contam hsc nodad
Logit estimates Number of obs = 153
LR chi2(5) = 66.56
Prob > chi2 = 0.0000
Log likelihood = -71.326227 Pseudo R2 = 0.3181
------------------------------------------------------------------------------
close | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
lived | .9611269 .0148794 -2.561 0.010 .9324019 .9907369
educ | .8214643 .0760781 -2.124 0.034 .6851041 .9849652
contam | 3.663985 1.746363 2.724 0.006 1.439614 9.325267
hsc | 9.76251 4.787244 4.647 0.000 3.733856 25.52498
nodad | .1771164 .128458 -2.387 0.017 .0427468 .7338607
------------------------------------------------------------------------------
predict b, dbeta
predict y, number
drop if y==3
(1 observation deleted)
logit close lived educ contam hsc nodad
Iteration 0: log likelihood = -104.03876
Iteration 1: log likelihood = -70.928977
Iteration 2: log likelihood = -67.960963
Iteration 3: log likelihood = -67.715507
Iteration 4: log likelihood = -67.712659
Iteration 5: log likelihood = -67.712659
Logit estimates Number of obs = 152
LR chi2(5) = 72.65
Prob > chi2 = 0.0000
Log likelihood = -67.712659 Pseudo R2 = 0.3492
------------------------------------------------------------------------------
close | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
lived | -.0446571 .0161192 -2.770 0.006 -.0762503 -.013064
educ | -.2236421 .0957586 -2.335 0.020 -.4113255 -.0359586
contam | 1.48958 .4975889 2.994 0.003 .5143239 2.464836
hsc | 2.49197 .5238655 4.757 0.000 1.465213 3.518728
nodad | -1.888628 .7622651 -2.478 0.013 -3.38264 -.3946154
_cons | 2.575416 1.370319 1.879 0.060 -.1103597 5.261191
------------------------------------------------------------------------------
with X pattern 115 deleted (n=152)
use http://www.ats.ucla.edu/stat/stata/examples/rwg/toxic, clear
logistic close lived educ contam hsc nodad
Logit estimates Number of obs = 153
LR chi2(5) = 66.56
Prob > chi2 = 0.0000
Log likelihood = -71.326227 Pseudo R2 = 0.3181
------------------------------------------------------------------------------
close | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
lived | .9611269 .0148794 -2.561 0.010 .9324019 .9907369
educ | .8214643 .0760781 -2.124 0.034 .6851041 .9849652
contam | 3.663985 1.746363 2.724 0.006 1.439614 9.325267
hsc | 9.76251 4.787244 4.647 0.000 3.733856 25.52498
nodad | .1771164 .128458 -2.387 0.017 .0427468 .7338607
------------------------------------------------------------------------------
predict b, dbeta
predict y, number
drop if y==115
(1 observation deleted)
logit close lived educ contam hsc nodad
Iteration 0: log likelihood = -104.03876
Iteration 1: log likelihood = -72.041161
Iteration 2: log likelihood = -69.387543
Iteration 3: log likelihood = -69.190424
Iteration 4: log likelihood = -69.188607
Iteration 5: log likelihood = -69.188607
Logit estimates Number of obs = 152
LR chi2(5) = 69.70
Prob > chi2 = 0.0000
Log likelihood = -69.188607 Pseudo R2 = 0.3350
------------------------------------------------------------------------------
close | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
lived | -.0355651 .0154349 -2.304 0.021 -.0658168 -.0053133
educ | -.2042185 .0940803 -2.171 0.030 -.3886124 -.0198246
contam | 1.451658 .4911553 2.956 0.003 .4890117 2.414305
hsc | 2.460228 .5164483 4.764 0.000 1.448008 3.472448
nodad | -1.90518 .7502902 -2.539 0.011 -3.375722 -.4346386
_cons | 2.183335 1.342716 1.626 0.104 -.4483392 4.815009
------------------------------------------------------------------------------
Figure 7.10, page 242.
use http://www.ats.ucla.edu/stat/stata/examples/rwg/toxic, clear
logistic close lived educ contam hsc nodad
Logit estimates Number of obs = 153
LR chi2(5) = 66.56
Prob > chi2 = 0.0000
Log likelihood = -71.326227 Pseudo R2 = 0.3181
------------------------------------------------------------------------------
close | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
lived | .9611269 .0148794 -2.561 0.010 .9324019 .9907369
educ | .8214643 .0760781 -2.124 0.034 .6851041 .9849652
contam | 3.663985 1.746363 2.724 0.006 1.439614 9.325267
hsc | 9.76251 4.787244 4.647 0.000 3.733856 25.52498
nodad | .1771164 .128458 -2.387 0.017 .0427468 .7338607
------------------------------------------------------------------------------
predict lhat
predict x, ddeviance
predict b, dbeta
graph twoway scatter x lhat [weight=b], msymbol(oh) xlabel(0(.2)1) ylabel(0(1)7)
UCLA Researchers are invited to our Statistical Consulting Services
We recommend others to our list of Other Resources for Statistical Computing Help
These pages are Copyrighted (c) by UCLA Academic Technology Services
