|
|
|
||||
|
|
|||||
This chapter makes use of the lowbwt file.
use lowbwt
In order to demonstrate the logistic regression diagnostic we need to compute the following model.
/* create dummy variable for the variable race */
xi i.race
i.race Irace_1-3 (naturally coded; Irace_1 omitted)
/* create dichotomous variable lwd from variable lwt */
generate lwd = (lwt<110)
/* create dichotomous variable ptd from variable ptl */
generate ptd=(ptl~=0)
/* create two interaction variables */
generate agelwd=age*lwd
generate smokelwd=smoke*lwd
/* run logistic regression model */
logit low age Irace_2 Irace_3 smoke ht ui lwd ptd agelwd smokelwd
Iteration 0: log likelihood = -117.336
Iteration 1: log likelihood = -97.135228
Iteration 2: log likelihood = -96.03855
Iteration 3: log likelihood = -96.006202
Iteration 4: log likelihood = -96.00616
Logit estimates Number of obs = 189
LR chi2(10) = 42.66
Prob > chi2 = 0.0000
Log likelihood = -96.00616 Pseudo R2 = 0.1818
------------------------------------------------------------------------------
low | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
age | -.0839782 .0455663 -1.843 0.065 -.1732864 .0053301
Irace_2 | 1.083103 .5189153 2.087 0.037 .0660474 2.100158
Irace_3 | .7596787 .4640335 1.637 0.102 -.1498103 1.669168
smoke | 1.153131 .4584383 2.515 0.012 .2546084 2.051653
ht | 1.359216 .661471 2.055 0.040 .062757 2.655676
ui | .7281685 .4794797 1.519 0.129 -.2115945 1.667932
lwd | -1.729949 1.868306 -0.926 0.354 -5.391762 1.931863
ptd | 1.231578 .4713903 2.613 0.009 .3076701 2.155486
agelwd | .1474112 .0828594 1.779 0.075 -.0149902 .3098127
smokelwd | -1.407375 .8186761 -1.719 0.086 -3.011951 .1972003
_cons | -.5117544 1.087536 -0.471 0.638 -2.643286 1.619777
------------------------------------------------------------------------------
Now we will compute various diagnostic statistics.
/* predicted probability of a positive score */ /* denoted pi-hat in Hosmer and Lemeshow */ predict p predict dx2, dx2 predict dd, ddeviance predict db, dbeta
Figure 5.3 -- page 162
graph twoway scatter dx2 p, ylabel(0(5)15) xlabel(0 .5 1)
Figure 5.4 -- page 163
graph twoway scatter dd p, ylabel(0(2)6) xlabel(0 .5 1)
Figure 5.5 -- page 164
graph twoway scatter db p, ylabel(0(.5)1.5) xlabel(0 .5 1)
Figure 5.6 -- page 165
graph twoway scatter dx2 p [w=db], ylabel(0(5)15) xlabel(0 .5 1) msymbol(oh)
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
