. do "C:\Users\DEFAUL~1.SID\AppData\Local\Temp\STD00000000.tmp"

. * do-file for lecture 4b of VHM 802/812, Winter 2016
. version 14 /* works also with version 13 */

. set more off

. cd "h:\vhm\vhm802\data_stata"
h:\vhm\vhm802\data_stata

.
. use nocardia.dta, clear

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

Iteration 0:   log likelihood = -74.859896
Iteration 1:   log likelihood = -54.156164
Iteration 2:   log likelihood = -53.993934
Iteration 3:   log likelihood = -53.993656
Iteration 4:   log likelihood = -53.993656

Logistic regression                             Number of obs     =        108
                                                LR chi2(3)        =      41.73
                                                Prob > chi2       =     0.0000
Log likelihood = -53.993656                     Pseudo R2         =     0.2787

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dneo |    2.212564    .5780423      3.83    0.000      1.079622     3.345506
       dclox |   -1.412516    .5572076     -2.53    0.011     -2.504623    -.3204093
       dcpct |    .0226682    .0071871      3.15    0.002      .0085817     .0367547
       _cons |   -2.984272    .7722467     -3.86    0.000     -4.497848    -1.470697
-------------+----------------------------------------------------------------

. logit casecont dneo dclox dcpct, or

Iteration 0:   log likelihood = -74.859896
Iteration 1:   log likelihood = -54.156164
Iteration 2:   log likelihood = -53.993934
Iteration 3:   log likelihood = -53.993656
Iteration 4:   log likelihood = -53.993656

Logistic regression                             Number of obs     =        108
                                                LR chi2(3)        =      41.73
                                                Prob > chi2       =     0.0000
Log likelihood = -53.993656                     Pseudo R2         =     0.2787

-------------+----------------------------------------------------------------
     casecont | Odds Ratio    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dneo |    9.139118    5.282797      3.83    0.000      2.943566     28.37493
       dclox |    .2435298    .1356966     -2.53    0.011      .0817064     .7258519
       dcpct |    1.022927    .0073519      3.15    0.002      1.008619     1.037439
       _cons |    .0505763    .0390574     -3.86    0.000      .0111329     .2297654
-------------+----------------------------------------------------------------

.
. * confounding effect by dcpct?
. logit casecont dneo dclox dcpct

Iteration 0:   log likelihood = -74.859896
Iteration 1:   log likelihood = -54.156164
Iteration 2:   log likelihood = -53.993934
Iteration 3:   log likelihood = -53.993656
Iteration 4:   log likelihood = -53.993656

Logistic regression                             Number of obs     =        108
                                                LR chi2(3)        =      41.73
                                                Prob > chi2       =     0.0000
Log likelihood = -53.993656                     Pseudo R2         =     0.2787

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dneo |    2.212564    .5780423      3.83    0.000      1.079622     3.345506
       dclox |   -1.412516    .5572076     -2.53    0.011     -2.504623    -.3204093
       dcpct |    .0226682    .0071871      3.15    0.002      .0085817     .0367547
       _cons |   -2.984272    .7722467     -3.86    0.000     -4.497848    -1.470697
-------------+----------------------------------------------------------------

. estimates store full

. logit casecont dneo dclox

Iteration 0:   log likelihood = -74.859896
Iteration 1:   log likelihood = -59.845472
Iteration 2:   log likelihood = -59.671317
Iteration 3:   log likelihood = -59.670766
Iteration 4:   log likelihood = -59.670766

Logistic regression                             Number of obs     =        108
                                                LR chi2(2)        =      30.38
                                                Prob > chi2       =     0.0000
Log likelihood = -59.670766                     Pseudo R2         =     0.2029

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dneo |    2.377409    .5504016      4.32    0.000      1.298642     3.456176
       dclox |   -1.009729    .5319365     -1.90    0.058     -2.052306     .0328471
       _cons |   -1.479612    .5013641     -2.95    0.003     -2.462267    -.4969559
-------------+----------------------------------------------------------------

. estimates store red

. estimates table *, se

-------------+--------------------------
Variable   | full     red
-------------+--------------------------
        dneo | 2.2125639     2.3774092
             | .57804231     .55040157
       dclox | -1.412516     -1.0097292
             | .55720756     .53193649
       dcpct | .02266825
             | .00718712
       _cons | -2.9842724     -1.4796115
             | .77224672     .50136411
-------------+--------------------------
legend: b/se

. di (2.3774092-2.2125639)/2.3774092 /* percent change for dneo */
.06933821

. di (1.412516-1.0097292)/1.0097292 /* percent change for dclox */
.39890577

. * check association: dcpct vs. exposure
. ranksum dcpct if casecont==0, by(dneo)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

        dneo |      obs    rank sum    expected
-------------+---------------------------------
          no |       29       796.5       797.5
         yes |       25       688.5       687.5
-------------+---------------------------------
    combined |       54        1485        1485

unadjusted variance     3322.92
adjustment for ties     -379.09
                     ----------
adjusted variance       2943.82

Ho: dcpct(dneo==no) = dcpct(dneo==yes)
             z =  -0.018
    Prob > |z| =   0.9853

. ranksum dcpct if casecont==0, by(dclox)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

       dclox |      obs    rank sum    expected
-------------+---------------------------------
          no |       35         788       962.5
         yes |       19         697       522.5
-------------+---------------------------------
    combined |       54        1485        1485

unadjusted variance     3047.92
adjustment for ties     -347.72
                     ----------
adjusted variance       2700.20

Ho: dcpct(dclox==no) = dcpct(dclox==yes)
             z =  -3.358
    Prob > |z| =   0.0008

. * check association in non-exposed units: dcpct vs. outcome
. ranksum dcpct if dneo==0, by(casecont)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

    casecont |      obs    rank sum    expected
-------------+---------------------------------
          no |       29         496       507.5
         yes |        5          99        87.5
-------------+---------------------------------
    combined |       34         595         595

unadjusted variance      422.92
adjustment for ties      -77.28
                     ----------
adjusted variance        345.64

Ho: dcpct(casecont==no) = dcpct(casecont==yes)
             z =  -0.619
    Prob > |z| =   0.5362

. ranksum dcpct if dclox==0, by(casecont)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

    casecont |      obs    rank sum    expected
-------------+---------------------------------
          no |       35        1042        1435
         yes |       46        2279        1886
-------------+---------------------------------
    combined |       81        3321        3321

unadjusted variance    11001.67
adjustment for ties    -2026.17
                     ----------
adjusted variance       8975.50

Ho: dcpct(casecont==no) = dcpct(casecont==yes)
             z =  -4.148
    Prob > |z| =   0.0000

.
. * interaction dneo*dclox
. generate neoclox=dneo*dclox

. logit casecont dcpct dneo dclox neoclox

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 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
       dcpct |    .0226175    .0077233      2.93    0.003      .0074802     .0377549
        dneo |    3.184002    .8372021      3.80    0.000      1.543116     4.824888
       dclox |    .4457043    1.026029      0.43    0.664     -1.565275     2.456683
     neoclox |   -2.551997    1.205077     -2.12    0.034     -4.913904     -.190089
       _cons |   -3.776896    .9932539     -3.80    0.000     -5.723638    -1.830154
-------------+----------------------------------------------------------------

. logit casecont dcpct dneo dclox neoclox, or

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 | Odds Ratio    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
       dcpct |    1.022875    .0078999      2.93    0.003      1.007508     1.038477
        dneo |    24.14318    20.21272      3.80    0.000      4.679147     124.5725
       dclox |     1.56159    1.602236      0.43    0.664      .2090306     11.66605
     neoclox |    .0779259    .0939067     -2.12    0.034      .0073438     .8268856
       _cons |    .0228936    .0227392     -3.80    0.000      .0032678     .1603889
-------------+----------------------------------------------------------------

. logit casecont dcpct i.dneo##i.dclox /* same thing with factor notation */

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 |      Coef.    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
-------------+----------------------------------------------------------------

. lincom 1.dneo+1.dneo#1.dclox /* log OR for dneo at dclox=1 */


 ( 1)  [casecont]1.dneo + [casecont]1.dneo#1.dclox = 0


-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |    .6320052    .8728059      0.72    0.469     -1.078663     2.342673
-------------+----------------------------------------------------------------

. lincom 1.dclox+1.dneo#1.dclox /* log OR for dclox at dneo=1 */


 ( 1)  [casecont]1.dclox + [casecont]1.dneo#1.dclox = 0


-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |   -2.106292    .6657654     -3.16    0.002     -3.411169    -.8014162
-------------+----------------------------------------------------------------

. lincom 1.dclox+1.dneo#1.dclox, or /* same, as OR */


 ( 1)  [casecont]1.dclox + [casecont]1.dneo#1.dclox = 0


-------------+----------------------------------------------------------------
     casecont | Odds Ratio    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |    .1216883    .0810159     -3.16    0.002      .0330026     .4486931
-------------+----------------------------------------------------------------

. * interaction plots, at dcpct=100
. margins i.dneo#i.dclox, at(dcpct=100) /* probability scale */

Adjusted predictions                            Number of obs     =        108
Model VCE    : OIM


Expression  : Pr(casecont), predict()


at  : dcpct           =         100


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
  dneo#dclox |
      no#no  |    .1801775    .1153654      1.56    0.118     -.0459344     .4062895
     no#yes  |    .2555094    .1269408      2.01    0.044      .0067101     .5043088
     yes#no  |    .8414233    .0528546     15.92    0.000      .7378302     .9450164
    yes#yes  |    .3923522    .1343778      2.92    0.004      .1289765     .6557278
-------------+----------------------------------------------------------------

. marginsplot


Variables that uniquely identify margins: dneo dclox


. marginsplot, x(dclox) /* switch roles of predictors in plot */


Variables that uniquely identify margins: dneo dclox


. margins i.dneo#i.dclox, at(dcpct=100) expression(predict(xb)) /* logit scale */

Adjusted predictions                            Number of obs     =        108
Model VCE    : OIM


Expression  : Linear prediction (log odds), predict(xb)


at  : dcpct           =         100


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
  dneo#dclox |
      no#no  |   -1.515145    .7810071     -1.94    0.052     -3.045891     .0156008
     no#yes  |   -1.069441    .6673214     -1.60    0.109     -2.377367     .2384851
     yes#no  |    1.668857    .3961219      4.21    0.000       .892472     2.445241
    yes#yes  |   -.4374356     .563637     -0.78    0.438     -1.542144     .6672726
-------------+----------------------------------------------------------------

. marginsplot


Variables that uniquely identify margins: dneo dclox


.
. * evaluating linearity graphically
. logit casecont numcow

Iteration 0:   log likelihood = -74.859896
Iteration 1:   log likelihood = -72.698945
Iteration 2:   log likelihood =  -72.69687
Iteration 3:   log likelihood =  -72.69687

Logistic regression                             Number of obs     =        108
                                                LR chi2(1)        =       4.33
                                                Prob > chi2       =     0.0375
Log likelihood =  -72.69687                     Pseudo R2         =     0.0289

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
      numcow |    .0147207    .0077021      1.91    0.056     -.0003751     .0298164
       _cons |   -.7101273    .4109623     -1.73    0.084     -1.515599     .0953439
-------------+----------------------------------------------------------------

. summarize numcow, detail

                          Herd size
-------------------------------------------------------------
      Percentiles      Smallest
 1%           16             16
 5%           18             16
10%           22             16       Obs                 108
25%           30             17       Sum of Wgt.         108

50%           41                      Mean           49.09259
                        Largest       Std. Dev.       30.0201
75%           54            135
90%           90            140       Variance       901.2063
95%          100            175       Skewness        2.24793
99%          175            190       Kurtosis       9.539434

. scatter casecont numcow, ylabel(0 1)

. tabulate numcow casecont /* little replication of numcow */

           |    Case - Control
 Herd size |        no        yes |     Total
-----------+----------------------+----------
        16 |         2          1 |         3
        17 |         0          2 |         2
        18 |         2          0 |         2
        20 |         1          1 |         2
        21 |         1          0 |         1
        22 |         2          0 |         2
        23 |         3          0 |         3
        24 |         1          1 |         2
        25 |         2          0 |         2
        26 |         0          1 |         1
        29 |         0          1 |         1
        30 |         3          5 |         8
        32 |         0          1 |         1
        34 |         2          0 |         2
        35 |         2          2 |         4
        36 |         1          0 |         1
        37 |         3          1 |         4
        38 |         1          0 |         1
        40 |         3          7 |        10
        41 |         1          2 |         3
        42 |         2          1 |         3
        43 |         1          0 |         1
        44 |         1          0 |         1
        45 |         0          2 |         2
        47 |         1          1 |         2
        50 |         6          8 |        14
        51 |         0          2 |         2
        53 |         1          0 |         1
        55 |         2          0 |         2
        56 |         1          0 |         1
        57 |         0          2 |         2
        60 |         2          1 |         3
        65 |         0          1 |         1
        72 |         0          1 |         1
        75 |         1          1 |         2
        80 |         1          0 |         1
        85 |         1          2 |         3
        90 |         2          1 |         3
        91 |         1          0 |         1
        97 |         0          1 |         1
       100 |         1          1 |         2
       135 |         0          1 |         1
       140 |         0          1 |         1
       175 |         0          1 |         1
       190 |         0          1 |         1
-----------+----------------------+----------
     Total |        54         54 |       108


. lowess casecont numcow, logit /* smoothed scatterplot */

. * added code for smoothed scatterplot with restricted range
. lowess casecont numcow, logit nograph generate(smoothy)

. twoway (line smoothy numcow if numcow<100, sort)

. * note that the following coding is totally wrong
. lowess casecont numcow if numcow<100, logit

.
. * evaluating linearity by categorization, using lintrend add-on command
. lintrend casecont numcow, groups(4) plot(log) /* linear trend plot */


The proportion and log odds of casecont by categories of numcow

  (Note: 4 numcow categories of equal sample size;
     Uses mean numcow value for each category)

  +------------------------------------------------------+
  | numcow   min   max    d   total   casecont   logodds |
  +------------------------------------------------------+
  |   23.6    16    30   12      29       0.41     -0.35 |
  |   37.9    32    41   13      26       0.50      0.00 |
  |   48.2    42    53   14      26       0.54      0.15 |
  |   88.1    55   190   15      27       0.56      0.22 |
  +------------------------------------------------------+

. egen numcow4=cut(numcow), at(16,32,42,55,200) /* note: left endpoints included */

. sort numcow

. browse numcow numcow4

. logit casecont i.numcow4 /* model with numcow categorized */

Iteration 0:   log likelihood = -74.859896
Iteration 1:   log likelihood = -74.182735
Iteration 2:   log likelihood = -74.182689
Iteration 3:   log likelihood = -74.182689

Logistic regression                             Number of obs     =        108
                                                LR chi2(3)        =       1.35
                                                Prob > chi2       =     0.7163
Log likelihood = -74.182689                     Pseudo R2         =     0.0090

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
     numcow4 |
         32  |    .3483068    .5440616      0.64    0.522     -.7180343     1.414648
         42  |    .5024575    .5449025      0.92    0.356     -.5655318     1.570447
         55  |    .5714504    .5405154      1.06    0.290     -.4879403     1.630841
             |
       _cons |   -.3483068     .377037     -0.92    0.356     -1.087286      .390672
-------------+----------------------------------------------------------------

. testparm i.numcow4 /* combined Wald test; could also have used LR test */


 ( 1)  [casecont]32.numcow4 = 0


 ( 2)  [casecont]42.numcow4 = 0


 ( 3)  [casecont]55.numcow4 = 0


           chi2(  3) =    1.34
         Prob > chi2 =    0.7199

.
. * evaluating linearity by polynomials
. generate numcowsq=numcow^2

. logit casecont numcow numcowsq

Iteration 0:   log likelihood = -74.859896
Iteration 1:   log likelihood = -72.611319
Iteration 2:   log likelihood = -72.526454
Iteration 3:   log likelihood = -72.525066
Iteration 4:   log likelihood = -72.525064

Logistic regression                             Number of obs     =        108
                                                LR chi2(2)        =       4.67
                                                Prob > chi2       =     0.0968
Log likelihood = -72.525064                     Pseudo R2         =     0.0312

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
      numcow |    .0009965    .0256399      0.04    0.969     -.0492568     .0512498
    numcowsq |    .0000981    .0001827      0.54    0.591       -.00026     .0004563
       _cons |    -.342618     .763331     -0.45    0.654     -1.838719     1.153483
-------------+----------------------------------------------------------------

. estat vce, corr /* strong correlation of estimates */

Correlation matrix of coefficients of logit model

             | casecont
e(V)   | numcow    numcowsq    _cons
-------------+------------------------------
casecont  |
numcow   |   1.0000
numcowsq   |  -0.9444      1.0000
_cons   |  -0.9435      0.8224      1.0000

. * code to illustrate reduction in collinearity by centering
. generate numcowct=numcow-75 /* mean numcow=49, but distrib is right-skewed */

. generate numcowctsq=numcowct^2

. logit casecont numcowct numcowctsq

Iteration 0:   log likelihood = -74.859896
Iteration 1:   log likelihood = -72.611319
Iteration 2:   log likelihood = -72.526454
Iteration 3:   log likelihood = -72.525066
Iteration 4:   log likelihood = -72.525064

Logistic regression                             Number of obs     =        108
                                                LR chi2(2)        =       4.67
                                                Prob > chi2       =     0.0968
Log likelihood = -72.525064                     Pseudo R2         =     0.0312

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
    numcowct |    .0157166    .0090151      1.74    0.081     -.0019527     .0333858
  numcowctsq |    .0000981    .0001827      0.54    0.591       -.00026     .0004563
       _cons |    .2841217    .3524342      0.81    0.420     -.4066367     .9748802
-------------+----------------------------------------------------------------

. estat vce, corr

Correlation matrix of coefficients of logit model

             | casecont
e(V)   | numcowct    numc~tsq    _cons
-------------+------------------------------
casecont  |
numcowct   |   1.0000
numcowctsq   |   0.3545      1.0000
_cons   |   0.4882     -0.4552      1.0000

.
. * other polynomials (advanced)
. * orthogonal polynomials
. orthpoly numcow, degree(2) generate(numcowop1 numcowop2)

. pwcorr numcowop1 numcowop2 /* no correlation */

             | numcow~1 numcow~2
-------------+------------------
   numcowop1 |   1.0000
   numcowop2 |  -0.0000   1.0000

. logit casecont numcowop1 numcowop2 /* same as quadratic model above */

Iteration 0:   log likelihood = -74.859896
Iteration 1:   log likelihood = -72.611319
Iteration 2:   log likelihood = -72.526454
Iteration 3:   log likelihood = -72.525066
Iteration 4:   log likelihood = -72.525064

Logistic regression                             Number of obs     =        108
                                                LR chi2(2)        =       4.67
                                                Prob > chi2       =     0.0968
Log likelihood = -72.525064                     Pseudo R2         =     0.0312

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
   numcowop1 |    .5146503    .3092086      1.66    0.096     -.0913874     1.120688
   numcowop2 |       .1636    .3046459      0.54    0.591      -.433495     .7606949
       _cons |    .0304329    .2025842      0.15    0.881     -.3666248     .4274906
-------------+----------------------------------------------------------------

. estat vce, corr

Correlation matrix of coefficients of logit model

             | casecont
e(V)   | numcow~1    numcow~2    _cons
-------------+------------------------------
casecont  |
numcowop1   |   1.0000
numcowop2   |   0.5800      1.0000
_cons   |   0.2283      0.2102      1.0000

. drop numcowop1 numcowop2

. * fractional polynomials
. fp <numcow>,  scale center replace: logit casecont <numcow>
(fitting 44 models)
(....10%....20%....30%....40%....50%....60%....70%....80%....90%....100%)

Fractional polynomial comparisons:
-------------+------------------------------------------------------
      numcow |   df    Deviance   Dev. dif.   P(*)   Powers
-------------+------------------------------------------------------
     omitted |    0    149.720      5.526    0.237
      linear |    1    145.394      1.200    0.753   1
       m = 1 |    2    144.798      0.604    0.739   3
       m = 2 |    4    144.194      0.000       --   -2 3
-------------+------------------------------------------------------


(*) P = sig. level of model with m = 2
based on
chi^2 of dev. dif.


Logistic regression                             Number of obs     =        108
                                                LR chi2(2)        =       5.53
                                                Prob > chi2       =     0.0631
Log likelihood = -72.096751                     Pseudo R2         =     0.0369

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
    numcow_1 |   -.0184979     .024051     -0.77    0.442     -.0656371     .0286412
    numcow_2 |    .6824194    .5482471      1.24    0.213     -.3921253     1.756964
       _cons |    .0015338    .2352174      0.01    0.995     -.4594838     .4625515
-------------+----------------------------------------------------------------

. fp plot, r(none)

. predict linp_fp, xb

. scatter linp_fp numcow

. * piecewise linear curve
. mkspline numcowpl1 50 numcowpl2 75 numcowpl3=numcow /* knots at 50,75 */

. logit casecont numcowpl1 numcowpl2 numcowpl3

Iteration 0:   log likelihood = -74.859896
Iteration 1:   log likelihood = -71.743541
Iteration 2:   log likelihood = -71.540299
Iteration 3:   log likelihood = -71.535504
Iteration 4:   log likelihood = -71.535501

Logistic regression                             Number of obs     =        108
                                                LR chi2(3)        =       6.65
                                                Prob > chi2       =     0.0840
Log likelihood = -71.535501                     Pseudo R2         =     0.0444

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
   numcowpl1 |    .0292991    .0205295      1.43    0.154     -.0109379     .0695361
   numcowpl2 |   -.0320893    .0328243     -0.98    0.328     -.0964238     .0322452
   numcowpl3 |    .0447032    .0333335      1.34    0.180     -.0206293     .1100356
       _cons |   -1.157369    .7869526     -1.47    0.141     -2.699768     .3850293
-------------+----------------------------------------------------------------

. predict linp_pwl, xb

. scatter linp_pwl numcow

. * close to significance with quadratic polynomial and piecewise linear (50,75)
.
. * reload clean dataset
. use nocardia.dta, clear

. * preparation for model building
. codebook dout /* exclude due to too little variation */

-------------
doutdry cows turned out
-------------

                  type:  numeric (byte)
label  :  dout

                 range:  [1,3]                        units:  1
         unique values:  3                        missing .:  0/108

            tabulation:  Freq.   Numeric  Label
                           101         1  pasture
                             3         2  yard
                             4         3  none

. codebook dcprep /* exclude due to too little variation */

-------------
dcprepMethod of dry cow preparation
-------------

                  type:  numeric (byte)
label  :  dcprep

                 range:  [1,4]                        units:  1
         unique values:  4                        missing .:  0/108

            tabulation:  Freq.   Numeric  Label
                             2         1  none
                             5         2  washed
                            95         3  washed &
                             6         4  dry cow

. codebook dbarn /* generate dichotomous barn type, due to too sparse category */

-------------
dbarndry cow housing
-------------

                  type:  numeric (byte)
label  :  dbarn

                 range:  [1,3]                        units:  1
         unique values:  3                        missing .:  0/108

            tabulation:  Freq.   Numeric  Label
                            35         1  freestal
                            67         2  tiestall
                             6         3  other

. generate dbarn2=dbarn

. replace dbarn2=1 if dbarn==3
(6 real changes made)

. codebook doth /* good distribution on two categories */

-------------
dothOther dry cow products used
-------------

                  type:  numeric (byte)
label  :  yesno

                 range:  [0,1]                        units:  1
         unique values:  2                        missing .:  0/108

            tabulation:  Freq.   Numeric  Label
                            48         0  no
                            60         1  yes

. * stepwise model selection procedures
. stepwise, pr(.1): logit casecont dneo dclox dcpct doth numcow dbarn2 bscc /* backwards elimination */


  begin with full model


p = 0.6989 >= 0.1000  removing bscc


p = 0.5239 >= 0.1000  removing numcow


Logistic regression                             Number of obs     =        107
                                                LR chi2(5)        =      52.92
                                                Prob > chi2       =     0.0000
Log likelihood = -47.703634                     Pseudo R2         =     0.3568

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dneo |    2.620729    .6839433      3.83    0.000      1.280225     3.961233
       dclox |   -1.624902    .6240902     -2.60    0.009     -2.848096    -.4017076
       dcpct |    .0267303    .0081368      3.29    0.001      .0107825     .0426782
        doth |   -1.028909     .555921     -1.85    0.064     -2.118494      .060676
      dbarn2 |   -1.433304    .6047608     -2.37    0.018     -2.618613    -.2479943
       _cons |   -.6277067    1.109318     -0.57    0.571      -2.80193     1.546517
-------------+----------------------------------------------------------------

. stepwise, pe(.099): logit casecont dneo dclox dcpct doth numcow dbarn2 bscc /* forward selection */


  begin with empty model


p = 0.0000 <  0.0990  adding  dneo


p = 0.0055 <  0.0990  adding  dcpct


p = 0.0052 <  0.0990  adding  dclox


p = 0.0210 <  0.0990  adding  dbarn2


p = 0.0642 <  0.0990  adding  doth


Logistic regression                             Number of obs     =        107
                                                LR chi2(5)        =      52.92
                                                Prob > chi2       =     0.0000
Log likelihood = -47.703634                     Pseudo R2         =     0.3568

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dneo |    2.620729    .6839433      3.83    0.000      1.280225     3.961233
       dcpct |    .0267303    .0081368      3.29    0.001      .0107825     .0426782
       dclox |   -1.624902    .6240902     -2.60    0.009     -2.848096    -.4017076
      dbarn2 |   -1.433304    .6047608     -2.37    0.018     -2.618613    -.2479943
        doth |   -1.028909     .555921     -1.85    0.064     -2.118494      .060676
       _cons |   -.6277067    1.109318     -0.57    0.571      -2.80193     1.546517
-------------+----------------------------------------------------------------

. stepwise, pe(.099) pr(.1): logit casecont dneo dclox dcpct doth numcow dbarn2 bscc /* backwise stepwise */


  begin with full model


p = 0.6989 >= 0.1000  removing bscc


p = 0.5239 >= 0.1000  removing numcow


Logistic regression                             Number of obs     =        107
                                                LR chi2(5)        =      52.92
                                                Prob > chi2       =     0.0000
Log likelihood = -47.703634                     Pseudo R2         =     0.3568

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dneo |    2.620729    .6839433      3.83    0.000      1.280225     3.961233
       dclox |   -1.624902    .6240902     -2.60    0.009     -2.848096    -.4017076
       dcpct |    .0267303    .0081368      3.29    0.001      .0107825     .0426782
        doth |   -1.028909     .555921     -1.85    0.064     -2.118494      .060676
      dbarn2 |   -1.433304    .6047608     -2.37    0.018     -2.618613    -.2479943
       _cons |   -.6277067    1.109318     -0.57    0.571      -2.80193     1.546517
-------------+----------------------------------------------------------------

. * conclusion: all point to same model
. * refit with all observations included
. logit casecont dneo dclox dcpct doth dbarn2

Iteration 0:   log likelihood = -74.859896
Iteration 1:   log likelihood = -49.438676
Iteration 2:   log likelihood = -49.019308
Iteration 3:   log likelihood = -49.017623
Iteration 4:   log likelihood = -49.017623

Logistic regression                             Number of obs     =        108
                                                LR chi2(5)        =      51.68
                                                Prob > chi2       =     0.0000
Log likelihood = -49.017623                     Pseudo R2         =     0.3452

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dneo |    2.668593    .6773553      3.94    0.000      1.341001     3.996185
       dclox |   -1.420506    .6011634     -2.36    0.018     -2.598764    -.2422469
       dcpct |    .0251328     .007934      3.17    0.002      .0095826     .0406831
        doth |   -1.097966    .5499217     -2.00    0.046     -2.175793    -.0201393
      dbarn2 |   -1.383821    .5986505     -2.31    0.021     -2.557154    -.2104872
       _cons |   -.5857322     1.09636     -0.53    0.593     -2.734558     1.563093
-------------+----------------------------------------------------------------

. * continue manually from model with interaction dneo*dclox included
. logit casecont i.dneo##i.dclox dcpct doth dbarn2

Iteration 0:   log likelihood = -74.859896
Iteration 1:   log likelihood = -48.325988
Iteration 2:   log likelihood = -47.571796
Iteration 3:   log likelihood = -47.565495
Iteration 4:   log likelihood = -47.565494

Logistic regression                             Number of obs     =        108
                                                LR chi2(6)        =      54.59
                                                Prob > chi2       =     0.0000
Log likelihood = -47.565494                     Pseudo R2         =     0.3646

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dneo |
        yes  |    3.542159     .934735      3.79    0.000      1.710112     5.374205
             |
       dclox |
        yes  |    .2270733    1.141361      0.20    0.842     -2.009953       2.4641
             |
  dneo#dclox |
    yes#yes  |   -2.218828    1.316287     -1.69    0.092     -4.798703     .3610475
             |
       dcpct |     .024149    .0082936      2.91    0.004      .0078938     .0404041
        doth |   -.8165712    .5667784     -1.44    0.150     -1.927436      .294294
      dbarn2 |   -1.420535    .6030892     -2.36    0.019     -2.602568    -.2385017
       _cons |   -1.355178    1.280715     -1.06    0.290     -3.865334     1.154977
-------------+----------------------------------------------------------------

. estat vce, corr /* look for correlation doth vs dneo#dclox */

Correlation matrix of coefficients of logit model

             | casecont
   | 1.   1.   1.dneo#
e(V)   | dneo    dclox    1.dclox    dcpct    doth    dbarn2    _cons
-------------+----------------------------------------------------------------------
casecont  |
1.dneo   |   1.0000
1.dclox   |   0.6412      1.0000
1.dneo#  |
1.dclox   |  -0.6622     -0.8445      1.0000
dcpct   |   0.0907     -0.1916      0.0300      1.0000
doth   |   0.0989      0.3412     -0.2729     -0.2363      1.0000
dbarn2   |  -0.3792     -0.0792      0.0846     -0.0856      0.1030      1.0000
_cons   |  -0.3874     -0.4065      0.4060     -0.3844     -0.3192     -0.5477      1.0000

. table casecont dneo dclox, by(doth) /* table to investigate change in doth estimate */

----------+-------------------------
Other dry |
cow       |
products  | Cloxacillin used on farm
used and  |and Neomycin used on farm
Case -    | --- no ---    --- yes --
Control   |   no   yes      no   yes
----------+-------------------------
no        |
       no |    6     6       6     2
      yes |         23       2     3
----------+-------------------------
yes       |
       no |   14     9       3     8
      yes |    2    21       1     2
----------+-------------------------

. logit casecont i.dneo##i.dclox dcpct dbarn2 /* final model */

Iteration 0:   log likelihood = -74.859896
Iteration 1:   log likelihood =  -49.42205
Iteration 2:   log likelihood = -48.660445
Iteration 3:   log likelihood = -48.652782
Iteration 4:   log likelihood =  -48.65278

Logistic regression                             Number of obs     =        108
                                                LR chi2(5)        =      52.41
                                                Prob > chi2       =     0.0000
Log likelihood =  -48.65278                     Pseudo R2         =     0.3501

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dneo |
        yes  |    3.758082    .9296064      4.04    0.000      1.936087     5.580077
             |
       dclox |
        yes  |    .7683728    1.064759      0.72    0.471     -1.318516     2.855262
             |
  dneo#dclox |
    yes#yes  |   -2.782955    1.257469     -2.21    0.027     -5.247549    -.3183622
             |
       dcpct |    .0221456     .008126      2.73    0.006      .0062189     .0380723
      dbarn2 |   -1.370958    .5895722     -2.33    0.020     -2.526499    -.2154181
       _cons |   -1.982021    1.239291     -1.60    0.110     -4.410987     .4469445
-------------+----------------------------------------------------------------

. * display statistics and compute tests for dclox
. estimates store full

. estimates stats /* display model-fit statistics */

Akaike's information criterion and Bayesian information criterion

-------------+---------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
full         |        108  -74.8599  -48.65278       6    109.3056   125.3983
-------------+---------------------------------------------------------------


Note: N=Obs used in calculating BIC; see [R] BIC note.


. testparm i.dclox i.dneo#i.dclox /* Wald test for dclox */


 ( 1)  [casecont]1.dclox = 0


 ( 2)  [casecont]1.dneo#1.dclox = 0


           chi2(  2) =    9.06
         Prob > chi2 =    0.0108

. logit casecont dneo dcpct dbarn2 /* without dclox */

Iteration 0:   log likelihood = -74.859896
Iteration 1:   log likelihood = -53.932561
Iteration 2:   log likelihood = -53.628384
Iteration 3:   log likelihood = -53.626938
Iteration 4:   log likelihood = -53.626938

Logistic regression                             Number of obs     =        108
                                                LR chi2(3)        =      42.47
                                                Prob > chi2       =     0.0000
Log likelihood = -53.626938                     Pseudo R2         =     0.2836

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dneo |    2.877515    .6503633      4.42    0.000      1.602827     4.152204
       dcpct |    .0188502    .0073545      2.56    0.010      .0044357     .0332647
      dbarn2 |   -1.430442    .5645149     -2.53    0.011     -2.536871    -.3240129
       _cons |   -1.221492    1.067064     -1.14    0.252     -3.312899     .8699153
-------------+----------------------------------------------------------------

. estimates store red

. lrtest full, stats /* LR test for dclox */

Likelihood-ratio test                                 LR chi2(2)  =      9.95
(Assumption: red nested in full)                      Prob > chi2 =    0.0069

Akaike's information criterion and Bayesian information criterion

-------------+---------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
red          |        108  -74.8599  -53.62694       4    115.2539   125.9824
full         |        108  -74.8599  -48.65278       6    109.3056   125.3983
-------------+---------------------------------------------------------------


Note: N=Obs used in calculating BIC; see [R] BIC note.


.
. * categorical predictor effects: effects of dbarn and dneo#dclox
. logit casecont i.dneo##i.dclox dcpct i.dbarn /* final model */

Iteration 0:   log likelihood = -74.859896
Iteration 1:   log likelihood = -49.387707
Iteration 2:   log likelihood = -48.572826
Iteration 3:   log likelihood =  -48.56345
Iteration 4:   log likelihood = -48.563447

Logistic regression                             Number of obs     =        108
                                                LR chi2(6)        =      52.59
                                                Prob > chi2       =     0.0000
Log likelihood = -48.563447                     Pseudo R2         =     0.3513

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dneo |
        yes  |    3.840981    .9613888      4.00    0.000      1.956693     5.725268
             |
       dclox |
        yes  |      .85191    1.086531      0.78    0.433     -1.277651     2.981471
             |
  dneo#dclox |
    yes#yes  |   -2.843304    1.269394     -2.24    0.025      -5.33127    -.3553377
             |
       dcpct |    .0215351    .0082369      2.61    0.009      .0053911     .0376792
             |
       dbarn |
   tiestall  |   -1.488824     .662023     -2.25    0.025     -2.786365    -.1912831
      other  |   -.4818957    1.137152     -0.42    0.672     -2.710672      1.74688
             |
       _cons |   -3.272424    1.063408     -3.08    0.002     -5.356666    -1.188183
-------------+----------------------------------------------------------------

. testparm i.dbarn /* overall test */


 ( 1)  [casecont]2.dbarn = 0


 ( 2)  [casecont]3.dbarn = 0


           chi2(  2) =    5.46
         Prob > chi2 =    0.0653

. lincom 2.dbarn-1.dbarn /* already in table of estimates */


 ( 1)  - [casecont]1b.dbarn + [casecont]2.dbarn = 0


-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |   -1.488824     .662023     -2.25    0.025     -2.786365    -.1912831
-------------+----------------------------------------------------------------

. lincom 3.dbarn-1.dbarn /* also in table of estimates */


 ( 1)  - [casecont]1b.dbarn + [casecont]3.dbarn = 0


-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |   -.4818957    1.137152     -0.42    0.672     -2.710672      1.74688
-------------+----------------------------------------------------------------

. lincom 3.dbarn-2.dbarn /* not in table of estimates */


 ( 1)  - [casecont]2.dbarn + [casecont]3.dbarn = 0


-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |    1.006929    1.033547      0.97    0.330     -1.018786     3.032643
-------------+----------------------------------------------------------------

. pwcompare dbarn i.dneo#i.dclox

Pairwise comparisons of marginal linear predictions


Margins  : asbalanced


-----------------------+------------------------------------------------
                       |                                 Una djusted
                       |   Contrast    Std. Err.      [95% Con f. Interval]
-----------------------+------------------------------------------------
casecont               |
                 dbarn |
 tiestall vs freestal  |   -1.488824     .662023      -2.786365    -.1912831
    other vs freestal  |   -.4818957    1.137152      -2.710672      1.74688
    other vs tiestall  |    1.006929    1.033547      -1.018786     3.032643
                       |
            dneo#dclox |
  (no#yes) vs (no#no)  |      .85191    1.086531      -1.277651     2.981471
  (yes#no) vs (no#no)  |    3.840981    .9613888       1.956693     5.725268
 (yes#yes) vs (no#no)  |    1.849587    1.077318      -.2619175     3.961091
 (yes#no) vs (no#yes)  |    2.989071    .8317627       1.358846     4.619295
(yes#yes) vs (no#yes)  |    .9976768    .9360594      -.8369658      2.83232
(yes#yes) vs (yes#no)  |   -1.991394    .6948229      -3.353222    -.6295658
-----------------------+------------------------------------------------

. pwcompare dbarn i.dneo#i.dclox, pv /* option pv to get P-values */

Pairwise comparisons of marginal linear predictions


Margins  : asbalanced


-----------------------+---------------------------------------
                       |                            Una djusted
                       |   Contrast    Std. Err.       z    P>|z|
-----------------------+---------------------------------------
casecont               |
                 dbarn |
 tiestall vs freestal  |   -1.488824     .662023     -2.25    0.025
    other vs freestal  |   -.4818957    1.137152     -0.42    0.672
    other vs tiestall  |    1.006929    1.033547      0.97    0.330
                       |
            dneo#dclox |
  (no#yes) vs (no#no)  |      .85191    1.086531      0.78    0.433
  (yes#no) vs (no#no)  |    3.840981    .9613888      4.00    0.000
 (yes#yes) vs (no#no)  |    1.849587    1.077318      1.72    0.086
 (yes#no) vs (no#yes)  |    2.989071    .8317627      3.59    0.000
(yes#yes) vs (no#yes)  |    .9976768    .9360594      1.07    0.287
(yes#yes) vs (yes#no)  |   -1.991394    .6948229     -2.87    0.004
-----------------------+---------------------------------------

. pwcompare dbarn i.dneo#i.dclox, pv mcompare(bon) /* bonferroni adjustment */

Pairwise comparisons of marginal linear predictions


Margins  : asbalanced


-------------+-------------
             |    Number of
             |  Comparisons
-------------+-------------
casecont     |
       dbarn |             3
  dneo#dclox |             6
-------------+-------------

-----------------------+---------------------------------------
                       |                            Bon ferroni
                       |   Contrast    Std. Err.       z    P>|z|
-----------------------+---------------------------------------
casecont               |
                 dbarn |
 tiestall vs freestal  |   -1.488824     .662023     -2.25    0.074
    other vs freestal  |   -.4818957    1.137152     -0.42    1.000
    other vs tiestall  |    1.006929    1.033547      0.97    0.990
                       |
            dneo#dclox |
  (no#yes) vs (no#no)  |      .85191    1.086531      0.78    1.000
  (yes#no) vs (no#no)  |    3.840981    .9613888      4.00    0.000
 (yes#yes) vs (no#no)  |    1.849587    1.077318      1.72    0.516
 (yes#no) vs (no#yes)  |    2.989071    .8317627      3.59    0.002
(yes#yes) vs (no#yes)  |    .9976768    .9360594      1.07    1.000
(yes#yes) vs (yes#no)  |   -1.991394    .6948229     -2.87    0.025
-----------------------+---------------------------------------

.
. * generalised linear models for binary/binomial data
. glm casecont dcpct dneo dclox, family(binomial) /* same as logit */

Iteration 0:   log likelihood = -54.197242
Iteration 1:   log likelihood = -53.993852
Iteration 2:   log likelihood = -53.993656
Iteration 3:   log likelihood = -53.993656

Generalized linear models                         No. of obs      =        108
Optimization     : ML                             Residual df     =        104
                                                  Scale parameter =          1
Deviance         =  107.9873114                   (1/df) Deviance =    1.03834
Pearson          =   117.392877                   (1/df) Pearson  =   1.128778

Variance function: V(u) =  u*(1-u)                [Bernoulli]
Link function    : g(u) =  ln(u/(1-u))            [Logit]

                                                  AIC             =   1.073957
Log likelihood   = -53.99365568                   BIC             =  -378.9543

-------------+----------------------------------------------------------------
             |                 OIM
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
       dcpct |    .0226682    .0071871      3.15    0.002      .0085817     .0367547
        dneo |    2.212564    .5780423      3.83    0.000      1.079622     3.345506
       dclox |   -1.412516    .5572076     -2.53    0.011     -2.504623    -.3204093
       _cons |   -2.984272    .7722467     -3.86    0.000     -4.497848    -1.470697
-------------+----------------------------------------------------------------

. glm casecont dcpct dneo dclox, family(binomial) link(probit)

Iteration 0:   log likelihood = -54.370785
Iteration 1:   log likelihood = -54.264374
Iteration 2:   log likelihood = -54.264207
Iteration 3:   log likelihood = -54.264207

Generalized linear models                         No. of obs      =        108
Optimization     : ML                             Residual df     =        104
                                                  Scale parameter =          1
Deviance         =   108.528413                   (1/df) Deviance =   1.043542
Pearson          =  114.9320864                   (1/df) Pearson  =   1.105116

Variance function: V(u) =  u*(1-u)                [Bernoulli]
Link function    : g(u) =  invnorm(u)             [Probit]

                                                  AIC             =   1.078967
Log likelihood   = -54.26420652                   BIC             =  -378.4132

-------------+----------------------------------------------------------------
             |                 OIM
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
       dcpct |    .0134078    .0041296      3.25    0.001      .0053139     .0215017
        dneo |    1.258567    .3168271      3.97    0.000      .6375976     1.879537
       dclox |    -.812481    .3213006     -2.53    0.011     -1.442219    -.1827433
       _cons |   -1.703551    .4132833     -4.12    0.000     -2.513571    -.8935307
-------------+----------------------------------------------------------------

.
end of do-file

. exit, clear