. do "C:\Users\hstryhn\AppData\Local\Temp\STD3dc4_000000.tmp"

. * do-file for lecture 5a of VHM 812/802, Winter 2025
. version 18 /* works also with versions 14-17 */

. set more off

. set scheme stcolor_alt

. cd "r:\"
r:\

.
. use nocardia.dta, clear

. keep id casecont dcpct dneo dclox /* drop variables not needed for now */

.
. * multiple logistic regression model
. logit casecont dcpct i.dneo##i.dclox

Iteration 0: Log likelihood = -74.859896
Iteration 1: Log likelihood = -52.23958
Iteration 2: Log likelihood = -51.712191
Iteration 3: Log likelihood = -51.70842
Iteration 4: Log likelihood = -51.708419

Logistic regression Number of obs = 108
LR chi2(4) = 46.30
Prob > chi2 = 0.0000
Log likelihood = -51.708419 Pseudo R2 = 0.3093

+

--------

casecont | Coefficient Std. err. z P>|z| [95% con f. interval]

+

--------

dcpct | .0226175 .0077233 2.93 0.003 .0074802 .0377549
|
dneo |
yes | 3.184002 .8372021 3.80 0.000 1.543116 4.824888
|
dclox |
yes | .4457043 1.026029 0.43 0.664 -1.565275 2.456683
|
dneo#dclox |
yes#yes | -2.551997 1.205077 -2.12 0.034 -4.913904 -.190089
|
_cons | -3.776896 .9932539 -3.80 0.000 -5.723638 -1.830154

+

--------

. estat ic

Akaike's information criterion and Bayesian information criterion

+

Model | N ll(null) ll(model) df AIC BIC

+

. | 108 -74.8599 -51.70842 5 113.4168 126.8275

+

Note: BIC uses N = number of observations. See [R] IC note .

.
. * covariate patterns
. predict cov, number

. codebook cov, c

Variable Obs Unique Mean Min Max Label

cov 108 30 22.2037 1 30 covariate pattern

. bysort cov: egen ncov=count(id)

. by cov: egen propcas=mean(casecont)

. by cov: gen within=_n

. order id cov ncov dcpct dneo dclox

. br cov ncov dcpct dneo dclox propcas if within==1

.
. * Pearson residuals
. predict pv /* predicted probabilities */
(option pr assumed; Pr(casecont))

. predict pearson, residual

. gen pearson_ind=(casecont-pv)/sqrt(pv*(1-pv))

. /* individual Pearson residuals also from:
> glm casecont dcpct i.dneo##i.dclox, fam(bin)
> predict pearson_ind, pearson
> */
. list id cov ncov dcpct dneo dclox propcas pv pearson pearson_ind if cov==9 /* VER 16.6 */

+-----------------------------------------
-
-
-
+
| id cov ncov dcpct dneo dclox propcas pv pearson pearson~d |
+-----------------------------------------
-
-
-
+
17. | 22 9 2 20 yes no .5 .4649217 .0994614 -.9321402 |
18. | 86 9 2 20 yes no .5 .4649217 .0994614 1.0728 |
+-----------------------------------------
-
-
-
+

.
. * goodness-of-fit tests
. * Pearson goodness-of-fit test
. estat gof

Goodness-of-fit test after logistic model
Variable: casecont

Number of observations = 108
Number of covariate patterns = 30
Pearson chi2(25) = 53.49
Prob > chi2 = 0.0008

. /* demonstrate test=sum of Pearson residuals^2 across covariate patterns
> generate pearson2=pearson^2
> total pearson2 if within==1 */
. * Hosmer-Lemeshow test
. estat gof, group(10) table /* VER 16.7 */

note: obs collapsed on 10 quantiles of estimated probabilities.

Goodness-of-fit test after logistic model
Variable: casecont

Table collapsed on quantiles of estimated probabilities
+-------+--------+-------+-------+-------+-------+-------+
| Group | Prob | Obs_1 | Exp_1 | Obs_0 | Exp_0 | Total |
+-------+--------+-------+-------+-------+-------+-------+
| 1 | 0.0387 | 1 | 0.3 | 10 | 10.7 | 11 |
| 2 | 0.1802 | 2 | 2.2 | 12 | 11.8 | 14 |
| 3 | 0.2555 | 3 | 3.0 | 9 | 9.0 | 12 |
| 4 | 0.3823 | 1 | 2.5 | 6 | 4.5 | 7 |
| 5 | 0.4119 | 4 | 3.9 | 6 | 6.1 | 10 |
+-------+--------+-------+-------+-------+-------+-------+
| 6 | 0.7509 | 8 | 8.5 | 6 | 5.5 | 14 |
| 10 | 0.8414 | 35 | 33.6 | 5 | 6.4 | 40 |
+-------+--------+-------+-------+-------+-------+-------+

Number of observations = 108
Number of groups = 7
Hosmer–Lemeshow chi2(5) = 3.85
Prob > chi2 = 0.5715

Warning: There are only 7 distinct quantiles because of ties.

. /* demonstration of calculation of expected values
> tab pv
> total pv if pv<0.0388
> total pv if pv>0.0388 & pv<0.182
> */
. * calculation of the deviance chi-square statistic
. estimates store red

. logit casecont i.cov, asis

Iteration 0: Log likelihood = -74.859896
Iteration 1: Log likelihood = -38.990447
Iteration 2: Log likelihood = -37.404488
Iteration 3: Log likelihood = -37.080184
Iteration 4: Log likelihood = -37.021569
Iteration 5: Log likelihood = -37.007145
Iteration 6: Log likelihood = -37.004043
Iteration 7: Log likelihood = -37.003335
Iteration 8: Log likelihood = -37.003217
Iteration 9: Log likelihood = -37.003204
Iteration 10: Log likelihood = -37.003202

Logistic regression Number of obs = 108
LR chi2(29) = 75.71
Prob > chi2 = 0.0000
Log likelihood = -37.003202 Pseudo R2 = 0.5057

+

--------

casecont | Coefficient Std. err. z P>|z| [95% con f. interval]

+

--------

cov |
2 | -3.86e-09 7686.89 -0.00 1.000 -15066.03 15066.03
3 | 35.52202 7686.89 0.00 0.996 -15030.51 15101.55
4 | -3.86e-09 7686.89 -0.00 1.000 -15066.03 15066.03
5 | -3.86e-09 4961.866 -0.00 1.000 -9725.079 9725.079
6 | -3.86e-09 7686.89 -0.00 1.000 -15066.03 15066.03
7 | 35.52202 7686.89 0.00 0.996 -15030.51 15101.55
8 | -3.86e-09 7686.89 -0.00 1.000 -15066.03 15066.03
9 | 17.76101 2717.726 0.01 0.995 -5308.885 5344.407
10 | -3.86e-09 7686.89 -0.00 1.000 -15066.03 15066.03
11 | 35.52202 7686.89 0.00 0.996 -15030.51 15101.55
12 | -3.86e-09 7686.89 -0.00 1.000 -15066.03 15066.03
13 | -3.86e-09 7686.89 -0.00 1.000 -15066.03 15066.03
14 | -3.86e-09 7686.89 -0.00 1.000 -15066.03 15066.03
15 | 35.52202 7686.89 0.00 0.996 -15030.51 15101.55
16 | -3.86e-09 7686.89 -0.00 1.000 -15066.03 15066.03
17 | 18.85932 2717.726 0.01 0.994 -5307.786 5345.505
18 | 17.76101 2717.726 0.01 0.995 -5308.885 5344.407
19 | 17.76101 2717.726 0.01 0.995 -5308.885 5344.407
20 | -3.86e-09 7686.89 -0.00 1.000 -15066.03 15066.03
21 | 35.52202 7686.89 0.00 0.996 -15030.51 15101.55
22 | -3.86e-09 7686.89 -0.00 1.000 -15066.03 15066.03
23 | 35.52202 7686.89 0.00 0.996 -15030.51 15101.55
24 | -3.86e-09 7686.89 -0.00 1.000 -15066.03 15066.03
25 | 35.52202 7686.89 0.00 0.996 -15030.51 15101.55
26 | -3.86e-09 7686.89 -0.00 1.000 -15066.03 15066.03
27 | 15.81523 2717.726 0.01 0.995 -5310.83 5342.461
28 | 16.25719 2717.726 0.01 0.995 -5310.388 5342.903
29 | 19.64793 2717.726 0.01 0.994 -5306.997 5346.293
30 | 17.53795 2717.726 0.01 0.995 -5309.108 5344.183
|
_cons | -17.76101 2717.726 -0.01 0.995 -5344.406 5308.884

+

--------

. lrtest red /* deviance statistic D */

Likelihood-ratio test
Assumption: red nested within .

LR chi2(25) = 29.41
Prob > chi2 = 0.2471

.
. * predictive ability
. logit casecont dcpct i.dneo##i.dclox

Iteration 0: Log likelihood = -74.859896
Iteration 1: Log likelihood = -52.23958
Iteration 2: Log likelihood = -51.712191
Iteration 3: Log likelihood = -51.70842
Iteration 4: Log likelihood = -51.708419

Logistic regression Number of obs = 108
LR chi2(4) = 46.30
Prob > chi2 = 0.0000
Log likelihood = -51.708419 Pseudo R2 = 0.3093

+

--------

casecont | Coefficient Std. err. z P>|z| [95% con f. interval]

+

--------

dcpct | .0226175 .0077233 2.93 0.003 .0074802 .0377549
|
dneo |
yes | 3.184002 .8372021 3.80 0.000 1.543116 4.824888
|
dclox |
yes | .4457043 1.026029 0.43 0.664 -1.565275 2.456683
|
dneo#dclox |
yes#yes | -2.551997 1.205077 -2.12 0.034 -4.913904 -.190089
|
_cons | -3.776896 .9932539 -3.80 0.000 -5.723638 -1.830154

+

--------

. estat classification, cutoff(0.5) /* option not needed: the default is 0.5 */

Logistic model for casecont

-------- True --------
Classified | D ~D | Total

+
+

+ | 41 9 | 50
- | 13 45 | 58

+
+

Total | 54 54 | 108

Classified + if predicted Pr(D) >= .5
True D defined as casecont != 0

Sensitivity Pr( +| D) 75.93%
Specificity Pr( -|~D) 83.33%
Positive predictive value Pr( D| +) 82.00%
Negative predictive value Pr(~D| -) 77.59%

False + rate for true ~D Pr( +|~D) 16.67%
False - rate for true D Pr( -| D) 24.07%
False + rate for classified + Pr(~D| +) 18.00%
False - rate for classified - Pr( D| -) 22.41%

Correctly classified 79.63%

. lsens /* two-curve ROC */

. lroc /* one-curve ROC, with AUC */

Logistic model for casecont

Number of observations = 108
Area under ROC curve = 0.8503

.
. * manual coding of leave-one-out cross-validation
. * note: code requires all observations to be used in model
. scalar cutoff=0.5 /* change if different cutoff is desired */

. generate obsno=_n /* id variable in data could be used as well */

. capture drop cvprob

. generate cvprob=.
(108 missing values generated)

. * cross-validation loop
. quietly forvalues i=1(1)`=_N'{

. generate estpos=cvprob>cutoff

. table estpos casecont /* classification table */

--------+--------------------
| Case - Control
| no yes Total
--------+--------------------
estpos |
0 | 45 13 58
1 | 9 41 50
Total | 54 54 108
--------+--------------------

. diagt casecont estpos /* uses add-on command diagt */

Case - | estpos
Control | Pos. Neg. | Total

+
+

Abnormal | 41 13 | 54
Normal | 9 45 | 54

+
+

Total | 50 58 | 108
True abnormal diagnosis defined as casecont = 1 (labelled yes)

[95% Confidence Interval]
---------------------------------------------------------------------------
Prevalence Pr(A) 50% 40% 59.8%
---------------------------------------------------------------------------
Sensitivity Pr(+|A) 75.9% 62.4% 86.5%
Specificity Pr(-|N) 83.3% 70.7% 92.1%
ROC area (Sens. + Spec.)/2 .796 .72 .873
---------------------------------------------------------------------------
Likelihood ratio (+) Pr(+|A)/Pr(+|N) 4.56 2.46 8.43
Likelihood ratio (-) Pr(-|A)/Pr(-|N) .289 .177 .471
Odds ratio LR(+)/LR(-) 15.8 6.16 40.3
Positive predictive value Pr(A|+) 82% 68.6% 91.4%
Negative predictive value Pr(N|-) 77.6% 64.7% 87.5%
---------------------------------------------------------------------------

. roctab casecont cvprob, sum

ROC Asymptotic normal
Obs area Std. err. [95% conf. interval]

108 0.7901 0.0482 0.69556 0.88469

.
. * residuals
. * reload data to work with clean dataset
. use nocardia.dta, clear

. keep id casecont dcpct dneo dclox /* drop variables not needed for now */

. logit casecont dcpct i.dneo##i.dclox

Iteration 0: Log likelihood = -74.859896
Iteration 1: Log likelihood = -52.23958
Iteration 2: Log likelihood = -51.712191
Iteration 3: Log likelihood = -51.70842
Iteration 4: Log likelihood = -51.708419

Logistic regression Number of obs = 108
LR chi2(4) = 46.30
Prob > chi2 = 0.0000
Log likelihood = -51.708419 Pseudo R2 = 0.3093

+

--------

casecont | Coefficient Std. err. z P>|z| [95% con f. interval]

+

--------

dcpct | .0226175 .0077233 2.93 0.003 .0074802 .0377549
|
dneo |
yes | 3.184002 .8372021 3.80 0.000 1.543116 4.824888
|
dclox |
yes | .4457043 1.026029 0.43 0.664 -1.565275 2.456683
|
dneo#dclox |
yes#yes | -2.551997 1.205077 -2.12 0.034 -4.913904 -.190089
|
_cons | -3.776896 .9932539 -3.80 0.000 -5.723638 -1.830154

+

--------

. predict cov, number

. bysort cov: egen ncov=count(id)

. by cov: egen propcas=mean(casecont)

. by cov: gen within=_n

. predict pv
(option pr assumed; Pr(casecont))

. predict pearson, residual

. predict stdres, rstandard

. predict devres, deviance

. summarize pearson stdres devres, d

Pearson residual

Percentiles Smallest
1% -1.362611 -1.362611
5% -1.160254 -1.362611
10% -.7691103 -1.362611 Obs 108
25% -.4060664 -1.160254 Sum of wgt. 108

50% .3200594 Mean .0860838
Largest Std. dev. .8452258
75% .4556087 1.226159
90% .4919042 1.329946 Variance .7144067
95% 1.013823 2.068801 Skewness 2.901567
99% 2.068801 5.902408 Kurtosis 22.13769

standardized Pearson residual

Percentiles Smallest
1% -2.145999 -2.145999
5% -2.145999 -2.145999
10% -2.145999 -2.145999 Obs 108
25% -.768505 -2.145999 Sum of wgt. 108

50% .5455669 Mean .1059213
Largest Std. dev. 1.238681
75% 1.007749 1.305849
90% 1.007749 1.393654 Variance 1.534332
95% 1.043455 2.145999 Skewness .4225766
99% 2.145999 5.975402 Kurtosis 6.280676

deviance residual

Percentiles Smallest
1% -1.700145 -1.700145
5% -1.0772 -1.700145
10% -.9640501 -1.700145 Obs 108
25% -.5629362 -1.213984 Sum of wgt. 108

50% .3178885 Mean .0251858
Largest Std. dev. .7229696
75% .4669968 1.189092
90% .5064911 1.427164 Variance .522685
95% 1.058873 1.824234 Skewness .0948717
99% 1.824234 2.675452 Kurtosis 3.914117

. twoway (scatter stdres cov [aweight=ncov], msymbol(Oh)) (scatter stdres cov, mlabel(cov) msize(tiny)), legend(off) yline(0) /* VER 16.11 */

. twoway (scatter stdres pv [aweight=ncov], msymbol(Oh)) (scatter stdres pv, mlabel(cov) msize(tiny)), legend(off) yline(0)

. * plot with individual Pearson residuals, for illustration only
. gen pearson_ind=(casecont-pv)/sqrt(pv*(1-pv))

. scatter pearson_ind pv, yline(0)

.
. * diagnostics
. predict lev, hat

. predict dx2, dx2

. predict ddev, ddeviance

. predict dbeta, dbeta

. summarize lev dx2 ddev dbeta

Variable | Obs Mean Std. dev. Min Max

+

lev | 108 .5231303 .3477705 .0202177 .9318134
dx2 | 108 1.531344 3.567081 .0113128 35.70543
ddev | 108 1.390318 1.521429 .011285 7.336179
dbeta | 108 8.024228 18.65432 .0005346 62.93452

. list cov ncov propcas pv pearson stdres devres lev dx2 ddev dbeta if cov==7 & within==1

+-------------
-
-
-
-
-
-
-
-
+
| cov ncov propcas pv pearson stdres devres lev dx2 ddev dbeta |
+-------------
-
-
-
-
-
-
-
-
+
15. | 7 1 1 .027903 5.902408 5.975402 2.675452 .0242821 35.70543 7.336179 .8885792 |
+-------------
-
-
-
-
-
-
-
-
+

. list cov ncov propcas pv pearson stdres devres lev dx2 ddev dbeta if cov==28 & within==1

+-------------
-
-
-
-
-
-
-
-
+
| cov ncov propcas pv pearson stdres devres lev dx2 ddev dbeta |
+-------------
-
-
-
-
-
-
-
-
+
51. | 28 11 .1818182 .2555094 -.5603756 -2.145999 -.5813009 .9318134 4.605314 4.955674 62.93452 |
+-------------
-
-
-
-
-
-
-
-
+

. * graphs for leverage
. twoway (scatter lev pv [aweight=ncov], msymbol(Oh)) (scatter lev pv, mlabel(cov) msize(tiny)), legend(off)

. twoway (scatter stdres lev[aweight=ncov], msymbol(Oh)) (scatter stdres lev, mlabel(cov) msize(tiny)), legend(off) yline(0)

. * graphs for dx2 and ddev
. twoway (scatter dx2 pv [aweight=ncov], msymbol(Oh)) (scatter dx2 pv, mlabel(cov) msize(tiny)), legend(off)

. twoway (scatter dx2 lev [aweight=ncov], msymbol(Oh)) (scatter dx2 lev, mlabel(cov) msize(tiny)), legend(off)

. twoway (scatter ddev pv [aweight=ncov], msymbol(Oh)) (scatter ddev pv, mlabel(cov) msize(tiny)), legend(off)

. twoway (scatter ddev lev [aweight=ncov], msymbol(Oh)) (scatter ddev lev, mlabel(cov) msize(tiny)), legend(off)

. * graphs for dbeta
. twoway (scatter dbeta pv [aweight=ncov], msymbol(Oh)) (scatter dbeta pv, mlabel(cov) msize(tiny)), legend(off)

. twoway (scatter dbeta lev [aweight=ncov], msymbol(Oh)) (scatter dbeta lev, mlabel(cov) msize(tiny)), legend(off)

. * complete table of most extreme covariate patterns (VER 16.12)
. list cov ncov propcas pv pearson stdres devres lev dx2 ddev dbeta if inlist(cov,7,27,28,29,30) & within==1

+-------------
-
-
-
-
-
-
-
-
+
| cov ncov propcas pv pearson stdres devres lev dx2 ddev dbeta |
+-------------
-
-
-
-
-
-
-
-
+
15. | 7 1 1 .027903 5.902408 5.975402 2.675452 .0242821 35.70543 7.336179 .8885792 |
43. | 27 8 .125 .1801775 -.4060664 -.768505 -.4250261 .7208095 .5906 .6470392 1.524801 |
51. | 28 11 .1818182 .2555094 -.5603756 -2.145999 -.5813009 .9318134 4.605314 4.955674 62.93452 |
62. | 29 38 .8684211 .8414233 .4556087 1.007749 .4669968 .7956007 1.015558 1.06696 3.95294 |
100. | 30 9 .4444444 .3923522 .3200594 .567266 .3178885 .6816627 .3217908 .3174404 .6890578 |
+-------------
-
-
-
-
-
-
-
-
+

.
. * analysis for grouped dcpct
. use nocardia.dta, clear

. egen dcpct3=cut(dcpct), at(0,50,100,1000)

. tab dcpct dcpct3

Pcnt. of |
cows dry | dcpct3
treated | 0 50 100 | Total

+
+

0 | 7 0 0 | 7
1 | 2 0 0 | 2
3 | 1 0 0 | 1
5 | 3 0 0 | 3
7 | 1 0 0 | 1
10 | 1 0 0 | 1
14 | 1 0 0 | 1
20 | 2 0 0 | 2
25 | 3 0 0 | 3
30 | 2 0 0 | 2
40 | 1 0 0 | 1
50 | 0 7 0 | 7
75 | 0 4 0 | 4
80 | 0 1 0 | 1
83 | 0 1 0 | 1
90 | 0 1 0 | 1
95 | 0 1 0 | 1
99 | 0 3 0 | 3
100 | 0 0 66 | 66

+
+

Total | 24 18 66 | 108

. logit casecont i.dcpct3 i.dneo##i.dclox

Iteration 0: Log likelihood = -74.859896
Iteration 1: Log likelihood = -52.081216
Iteration 2: Log likelihood = -51.634967
Iteration 3: Log likelihood = -51.632242
Iteration 4: Log likelihood = -51.632242

Logistic regression Number of obs = 108
LR chi2(5) = 46.46
Prob > chi2 = 0.0000
Log likelihood = -51.632242 Pseudo R2 = 0.3103

+

--------

casecont | Coefficient Std. err. z P>|z| [95% con f. interval]

+

--------

dcpct3 |
50 | 1.361002 .819178 1.66 0.097 -.2445579 2.966561
100 | 2.026562 .6855237 2.96 0.003 .6829604 3.370164
|
dneo |
yes | 3.19238 .8361783 3.82 0.000 1.5535 4.831259
|
dclox |
yes | .4529145 1.026657 0.44 0.659 -1.559296 2.465125
|
dneo#dclox |
yes#yes | -2.532558 1.207714 -2.10 0.036 -4.899634 -.1654829
|
_cons | -3.531226 .9364287 -3.77 0.000 -5.366593 -1.69586

+

--------

. estimates store red

. estat ic

Akaike's information criterion and Bayesian information criterion

+

Model | N ll(null) ll(model) df AIC BIC

+

red | 108 -74.8599 -51.63224 6 115.2645 131.3573

+

Note: BIC uses N = number of observations. See [R] IC note .

. estat gof

Goodness-of-fit test after logistic model
Variable: casecont

Number of observations = 108
Number of covariate patterns = 11
Pearson chi2(5) = 8.22
Prob > chi2 = 0.1444

. * recompute residuals and diagnosticd
. predict cov, number

. bysort cov: egen ncov=count(id)

. by cov: egen propcas=mean(casecont)

. by cov: gen within=_n

. list cov ncov propcas dneo dclox dcpct3 if within==1, sep(20) noobs

+-------------
------------------------+
| cov ncov propcas dneo dclox dcpct3 |
+-------------
------------------------+
| 1 12 .0833333 no no 0 |
| 2 11 .3636364 yes no 0 |
| 3 1 0 yes yes 0 |
| 4 2 0 no no 50 |
| 5 1 1 no yes 50 |
| 6 10 .7 yes no 50 |
| 7 5 .2 yes yes 50 |
| 8 8 .125 no no 100 |
| 9 11 .1818182 no yes 100 |
| 10 38 .8684211 yes no 100 |
| 11 9 .4444444 yes yes 100 |
+-------------
------------------------+

. predict pv
(option pr assumed; Pr(casecont))

. predict pearson, residual

. predict stdres, rstandard

. predict devres, deviance

. predict lev, hat

. predict dx2, dx2

. predict ddev, ddeviance

. predict dbeta, dbeta

. list cov ncov propcas pv pearson stdres devres lev dx2 ddev dbeta if within==1, noobs sep(20)

+-------------
-
-
-
-
-
-
-
-
+
| cov ncov propcas pv pearson stdres devres lev dx2 ddev dbeta |
+-------------
-
-
-
-
-
-
-
-
+
| 1 12 .0833333 .0284367 1.144093 1.358481 .9331458 .2907239 1.845472 1.227676 .7564373 |
| 2 11 .3636364 .4160897 -.3529414 -1.073387 -.355453 .8918833 1.152159 1.168615 9.504465 |
| 3 1 0 .0817735 -.2984224 -.3069953 -.4130647 .0550704 .0942461 .1805663 .0054927 |
| 4 2 0 .1024564 -.4778116 -.5232843 -.6575516 .1662458 .2738265 .5185871 .0545994 |
| 5 1 1 .152218 2.359985 2.496497 1.940331 .1063734 6.232499 4.213039 .7418891 |
| 6 10 .7 .7353922 -.2537148 -.4654696 -.2503426 .7028956 .2166619 .2109408 .5125833 |
| 7 5 .2 .2577896 -.2954191 -.4160336 -.3036099 .4957797 .1730839 .1828149 .1701865 |
| 8 8 .125 .1817309 -.4161044 -.8012637 -.4359247 .7303169 .6420236 .7046434 1.738636 |
| 9 11 .1818182 .2588893 -.5835651 -2.496497 -.6060105 .9453593 6.232499 6.721155 107.8308 |
| 10 38 .8684211 .8439235 .4160969 1.080066 .4256602 .8515815 1.166543 1.220782 6.69328 |
| 11 9 .4444444 .4032532 .2519084 .5182928 .2506801 .76377 .2686274 .2660142 .8685164 |
+-------------
-
-
-
-
-
-
-
-
+

. logit casecont i.cov, asis

Iteration 0: Log likelihood = -74.859896
Iteration 1: Log likelihood = -48.962127
Iteration 2: Log likelihood = -48.557744
Iteration 3: Log likelihood = -48.489928
Iteration 4: Log likelihood = -48.475495
Iteration 5: Log likelihood = -48.472329
Iteration 6: Log likelihood = -48.471744
Iteration 7: Log likelihood = -48.471685
Iteration 8: Log likelihood = -48.471673
Iteration 9: Log likelihood = -48.47167

Logistic regression Number of obs = 108
LR chi2(10) = 52.78
Prob > chi2 = 0.0000
Log likelihood = -48.47167 Pseudo R2 = 0.3525

+

--------

casecont | Coefficient Std. err. z P>|z| [95% con f. interval]

+

--------

cov |
2 | 1.838154 1.218094 1.51 0.131 -.5492668 4.225576
3 | -13.21623 2457.868 -0.01 0.996 -4830.549 4804.117
4 | -13.21623 1737.975 -0.01 0.994 -3419.585 3393.153
5 | 18.01197 2457.868 0.01 0.994 -4799.321 4835.345
6 | 3.245334 1.251842 2.59 0.010 .7917691 5.698899
7 | 1.011361 1.530048 0.66 0.509 -1.987478 4.0102
8 | .4518912 1.494591 0.30 0.762 -2.477453 3.381236
9 | .8935955 1.304636 0.68 0.493 -1.663443 3.450634
10 | 4.285027 1.149436 3.73 0.000 2.032173 6.537881
11 | 2.174692 1.241325 1.75 0.080 -.2582602 4.607645
|
_cons | -2.39787 1.044455 -2.30 0.022 -4.444964 -.3507759

+

--------

. lrtest red

Likelihood-ratio test
Assumption: red nested within .

LR chi2(5) = 6.32
Prob > chi2 = 0.2762

.
end of do-file

. exit, clear