-----------------------------------------------------------------
      name:  <unnamed>
       log:  C:\vhm812-data\l5a_log_reg_dx2017.txt
  log type:  text
 opened on:   5 Feb 2018, 15:12:10

. 
. * open the Nocardia dataset
. use nocardia.dta, clear

. sum dcpct

    Variable |        Obs        Mean    Std. Dev.       Min     
>    Max
-------------+---------------------------------------------------
> ------
       dcpct |        108    75.56481     37.3964          0     
>    100

. tab dcpct

   Pcnt. of |
   cows dry |
    treated |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |          7        6.48        6.48
          1 |          2        1.85        8.33
          3 |          1        0.93        9.26
          5 |          3        2.78       12.04
          7 |          1        0.93       12.96
         10 |          1        0.93       13.89
         14 |          1        0.93       14.81
         20 |          2        1.85       16.67
         25 |          3        2.78       19.44
         30 |          2        1.85       21.30
         40 |          1        0.93       22.22
         50 |          7        6.48       28.70
         75 |          4        3.70       32.41
         80 |          1        0.93       33.33
         83 |          1        0.93       34.26
         90 |          1        0.93       35.19
         95 |          1        0.93       36.11
         99 |          3        2.78       38.89
        100 |         66       61.11      100.00
------------+-----------------------------------
      Total |        108      100.00

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

. tab dcpct3

     dcpct3 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         24       22.22       22.22
         50 |         18       16.67       38.89
        100 |         66       61.11      100.00
------------+-----------------------------------
      Total |        108      100.00

. 
. * residuals one per covariate pattern
. * fitting a logistic model
. logit casecont dneo##dclox i.dcpct3

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 |      Coef.   Std. Err.      z    P>|z|     [95% Co
> n                                                              
>  f. Interval]
-------------+---------------------------------------------------
> -------------
        dneo |
        yes  |    3.19238   .8361783     3.82   0.000       1.553
> 5                                                              
>      4.831259
             |
       dclox |
        yes  |   .4529145   1.026657     0.44   0.659    -1.55929
> 6                                                              
>      2.465125
             |
  dneo#dclox |
    yes#yes  |  -2.532558   1.207714    -2.10   0.036    -4.89963
> 4                                                              
>     -.1654829
             |
      dcpct3 |
         50  |   1.361002    .819178     1.66   0.097    -.244557
> 9                                                              
>      2.966561
        100  |   2.026562   .6855237     2.96   0.003     .682960
> 4                                                              
>      3.370164
             |
       _cons |  -3.531226   .9364287    -3.77   0.000    -5.36659
> 3                                                              
>      -1.69586
-----------------------------------------------------------------
> -------------

. 
. * examining the covariate patterns
. predict cov, num

. predict pv, p

. sort cov

. * generate a count of the number of obs. in each cov. pattern
. quietly by cov: gen cnt=_N

. br cov cnt dcpct dneo dclox pv casecont

. 
. * examining Pearson residuals
. predict pear, res /*one per covariate pattern*/

. format pv pear  %5.3f

. sort pear

. summ pear

    Variable |        Obs        Mean    Std. Dev.       Min     
>    Max
-------------+---------------------------------------------------
> ------
        pear |        108    .1413821    .5669692  -.5835651   2.
> 359985

. list cov cnt dcpct dneo dclox pv casecont pear  if abs(pear)>2,
>  noobs sep(4)

  +-------------------------------------------------------------+
  | cov   cnt   dcpct   dneo   dclox      pv   casecont    pear |
  |-------------------------------------------------------------|
  |   4     1      83     no     yes   0.152        yes   2.360 |
  +-------------------------------------------------------------+

. 
. * Goodness-of-fit tests
. * Pearson GOF  
. **stata post estimation command
. estat gof

Logistic model for casecont, goodness-of-fit test

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

. * Hosmer - Lemeshow Test
. estat gof, g(10) table

Logistic model for casecont, goodness-of-fit test

  (Table collapsed on quantiles of estimated probabilities)
  (There are only 7 distinct quantiles because of ties)
  +--------------------------------------------------------+
  | Group |   Prob | Obs_1 | Exp_1 | Obs_0 | Exp_0 | Total |
  |-------+--------+-------+-------+-------+-------+-------|
  |     1 | 0.0284 |     1 |   0.3 |    11 |  11.7 |    12 |
  |     2 | 0.1817 |     2 |   1.9 |    10 |  10.1 |    12 |
  |     3 | 0.2589 |     3 |   4.1 |    13 |  11.9 |    16 |
  |     4 | 0.4033 |     4 |   3.6 |     5 |   5.4 |     9 |
  |     5 | 0.4161 |     4 |   4.6 |     7 |   6.4 |    11 |
  |-------+--------+-------+-------+-------+-------+-------|
  |     6 | 0.7354 |     7 |   7.4 |     3 |   2.6 |    10 |
  |    10 | 0.8439 |    33 |  32.1 |     5 |   5.9 |    38 |
  +--------------------------------------------------------+

       number of observations =       108
             number of groups =         7
      Hosmer-Lemeshow chi2(5) =         2.16
                  Prob > chi2 =         0.8262

. 
. * Evaluating Important Observations in a Logistic Model
. * fitting a logistic model
. logit casecont i.dneo##dclox i.dcpct3 

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 |      Coef.   Std. Err.      z    P>|z|     [95% Co
> n                                                              
>  f. Interval]
-------------+---------------------------------------------------
> -------------
        dneo |
        yes  |    3.19238   .8361783     3.82   0.000       1.553
> 5                                                              
>      4.831259
             |
       dclox |
        yes  |   .4529145   1.026657     0.44   0.659    -1.55929
> 6                                                              
>      2.465125
             |
  dneo#dclox |
    yes#yes  |  -2.532558   1.207714    -2.10   0.036    -4.89963
> 4                                                              
>     -.1654829
             |
      dcpct3 |
         50  |   1.361002    .819178     1.66   0.097    -.244557
> 9                                                              
>      2.966561
        100  |   2.026562   .6855237     2.96   0.003     .682960
> 4                                                              
>      3.370164
             |
       _cons |  -3.531226   .9364287    -3.77   0.000    -5.36659
> 3                                                              
>      -1.69586
-----------------------------------------------------------------
> -------------

. * predicting residuals and influential statistics
. capture drop cov 

. capture drop pv  

. capture drop cnt

. capture drop lev 

. capture drop pear_std 

. capture drop dx2

. capture drop db

. predict pv, p

. predict pear_std, rstandard

. predict lev, hat

. predict dx2, dx2 

. predict db, dbeta

. predict cov, num

. 
. **additional variables for listings and formatting
. bysort cov: gen cnt=_N

. bysort cov:gen wcov=_n

. bysort cov: egen opr=mean(casecont)

. *format all the listed variables to 3 decimals
. foreach var in opr pv pear_std lev dx2 db {
  2. format `var' %4.3f
  3. }

. 
. * Identifying highest leverage points
. summ lev, d

                          leverage
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .1063734       .0550704
 5%     .2907239       .1063734
10%     .2907239       .1662458       Obs                 108
25%     .7028956       .1662458       Sum of Wgt.         108

50%     .8515815                      Mean            .729414
                        Largest       Std. Dev.      .2256387
75%     .8515815       .9453593
90%     .9453593       .9453593       Variance       .0509128
95%     .9453593       .9453593       Skewness      -1.434562
99%     .9453593       .9453593       Kurtosis       3.850434

. * graph of stand. resid. vs leverage
. scatter lev pv, mlabel(cov) xline(0.1 0.9) xlabel(0(0.1)1)

. scatter pear_std  lev , mlabel(cov) yline(-2 2)

. sort lev pv 

. list cov cnt dcpct3 dneo dclox opr pv pear_std lev if  pv>0.1 &
>  pv<0.9 & wcov==1, noobs 

  +---------------------------------------------------+
  | cov | cnt | dcpct3 | dneo | dclox |   opr |    pv |
  |   4 |   1 |     50 |   no |   yes | 1.000 | 0.152 |
  |---------------------------------------------------|
  |         pear_std         |           lev          |
  |            2.496         |         0.106          |
  +---------------------------------------------------+

  +---------------------------------------------------+
  | cov | cnt | dcpct3 | dneo | dclox |   opr |    pv |
  |   2 |   2 |     50 |   no |    no | 0.000 | 0.102 |
  |---------------------------------------------------|
  |         pear_std         |           lev          |
  |           -0.523         |         0.166          |
  +---------------------------------------------------+

  +---------------------------------------------------+
  | cov | cnt | dcpct3 | dneo | dclox |   opr |    pv |
  |  10 |   5 |     50 |  yes |   yes | 0.200 | 0.258 |
  |---------------------------------------------------|
  |         pear_std         |           lev          |
  |           -0.416         |         0.496          |
  +---------------------------------------------------+

  +---------------------------------------------------+
  | cov | cnt | dcpct3 | dneo | dclox |   opr |    pv |
  |   7 |  10 |     50 |  yes |    no | 0.700 | 0.735 |
  |---------------------------------------------------|
  |         pear_std         |           lev          |
  |           -0.465         |         0.703          |
  +---------------------------------------------------+

  +---------------------------------------------------+
  | cov | cnt | dcpct3 | dneo | dclox |   opr |    pv |
  |   3 |   8 |    100 |   no |    no | 0.125 | 0.182 |
  |---------------------------------------------------|
  |         pear_std         |           lev          |
  |           -0.801         |         0.730          |
  +---------------------------------------------------+

  +---------------------------------------------------+
  | cov | cnt | dcpct3 | dneo | dclox |   opr |    pv |
  |  11 |   9 |    100 |  yes |   yes | 0.444 | 0.403 |
  |---------------------------------------------------|
  |         pear_std         |           lev          |
  |            0.518         |         0.764          |
  +---------------------------------------------------+

  +---------------------------------------------------+
  | cov | cnt | dcpct3 | dneo | dclox |   opr |    pv |
  |   8 |  38 |    100 |  yes |    no | 0.868 | 0.844 |
  |---------------------------------------------------|
  |         pear_std         |           lev          |
  |            1.080         |         0.852          |
  +---------------------------------------------------+

  +---------------------------------------------------+
  | cov | cnt | dcpct3 | dneo | dclox |   opr |    pv |
  |   6 |  11 |      0 |  yes |    no | 0.364 | 0.416 |
  |---------------------------------------------------|
  |         pear_std         |           lev          |
  |           -1.073         |         0.892          |
  +---------------------------------------------------+

  +---------------------------------------------------+
  | cov | cnt | dcpct3 | dneo | dclox |   opr |    pv |
  |   5 |  11 |    100 |   no |   yes | 0.182 | 0.259 |
  |---------------------------------------------------|
  |         pear_std         |           lev          |
  |           -2.496         |         0.945          |
  +---------------------------------------------------+

. 
. * residual  
. sort pear_std

. twoway (scatter pear_std cov [aweight=cnt], msymbol(Oh) mlcolor
> (black) mlwidth(medium)) ///
>                 (scatter pear_std cov, msize(vtiny) mlabel(cov)
> ), legend(off) yline( -2 2)

. list cov cnt dcpct3 dneo dclox opr pv pear_std if wcov==1 & abs
> (pear_std)>2 ,noobs 

  +---------------------------------------------------+
  | cov | cnt | dcpct3 | dneo | dclox |   opr |    pv |
  |   5 |  11 |    100 |   no |   yes | 0.182 | 0.259 |
  |---------------------------------------------------|
  |                     pear_std                      |
  |                       -2.496                      |
  +---------------------------------------------------+

  +---------------------------------------------------+
  | cov | cnt | dcpct3 | dneo | dclox |   opr |    pv |
  |   4 |   1 |     50 |   no |   yes | 1.000 | 0.152 |
  |---------------------------------------------------|
  |                     pear_std                      |
  |                        2.496                      |
  +---------------------------------------------------+

. 
. * evaluating delta chisq 
. scatter dx2 pv, mlabel(cov) yline(3.84) /*delta chi2*/

. sort dx2

. list cov cnt dcpct3 dneo dclox pv dx2 lev pear if dx2>3.84 & wc
> ov==1, noobs 

  +-----------------------------------------------------------+
  | cov | cnt | dcpct3 | dneo | dclox |    pv |   dx2 |   lev |
  |   4 |   1 |     50 |   no |   yes | 0.152 | 6.232 | 0.106 |
  |-----------------------------------------------------------|
  |                            pear                           |
  |                           2.360                           |
  +-----------------------------------------------------------+

  +-----------------------------------------------------------+
  | cov | cnt | dcpct3 | dneo | dclox |    pv |   dx2 |   lev |
  |   5 |  11 |    100 |   no |   yes | 0.259 | 6.232 | 0.945 |
  |-----------------------------------------------------------|
  |                            pear                           |
  |                          -0.584                           |
  +-----------------------------------------------------------+

. 
. * evaluating delta betas
. sort db

. summ db, d

                      Pregibon's dbeta
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .0545994       .0054927
 5%     .1701865       .0545994
10%     .5125833       .0545994       Obs                 108
25%     .7564373       .1701865       Sum of Wgt.         108

50%      6.69328                      Mean           14.65434
                        Largest       Std. Dev.      31.68869
75%      6.69328       107.8308
90%     107.8308       107.8308       Variance       1004.173
95%     107.8308       107.8308       Skewness       2.581599
99%     107.8308       107.8308       Kurtosis        7.77457

. scatter db pv, ml(cov) yline(1)

. scatter db lev, ml(cov) yline(1)

. scatter dx2 pv [aweight=db], msymbol(Oh)  || scatter dx2 pv, ml
> (cov) yline(3.84) legend(off) ///
>         ytitle("Delta Chi2")

. sort db 

. l cov cnt dcpct dneo dclox opr pv lev dx2 db if db > abs(1) & w
> cov==1, noobs 

  +----------------------------------------------------------+
  | cov | cnt | dcpct | dneo | dclox |   opr |    pv |   lev |
  |   3 |   8 |   100 |   no |    no | 0.125 | 0.182 | 0.730 |
  |----------------------------------------------------------|
  |             dx2           |                db            |
  |           0.642           |             1.739            |
  +----------------------------------------------------------+

  +----------------------------------------------------------+
  | cov | cnt | dcpct | dneo | dclox |   opr |    pv |   lev |
  |   8 |  38 |   100 |  yes |    no | 0.868 | 0.844 | 0.852 |
  |----------------------------------------------------------|
  |             dx2           |                db            |
  |           1.167           |             6.693            |
  +----------------------------------------------------------+

  +----------------------------------------------------------+
  | cov | cnt | dcpct | dneo | dclox |   opr |    pv |   lev |
  |   6 |  11 |    30 |  yes |    no | 0.364 | 0.416 | 0.892 |
  |----------------------------------------------------------|
  |             dx2           |                db            |
  |           1.152           |             9.504            |
  +----------------------------------------------------------+

  +----------------------------------------------------------+
  | cov | cnt | dcpct | dneo | dclox |   opr |    pv |   lev |
  |   5 |  11 |   100 |   no |   yes | 0.182 | 0.259 | 0.945 |
  |----------------------------------------------------------|
  |             dx2           |                db            |
  |           6.232           |           107.831            |
  +----------------------------------------------------------+

. br cov cnt dcpct dneo dclox opr pv lev dx2 db  

. 
. * dropping the highest db covariate pattern and refitting the m
> odel
. logit casecont dneo##dclox i.dcpct3 if cov~=5, asis

Iteration 0:   log likelihood =  -66.98248  
Iteration 1:   log likelihood = -44.531702  
Iteration 2:   log likelihood = -44.212331  
Iteration 3:   log likelihood = -44.194711  
Iteration 4:   log likelihood = -44.190532  
Iteration 5:   log likelihood = -44.189712  
Iteration 6:   log likelihood = -44.189579  
Iteration 7:   log likelihood = -44.189547  
Iteration 8:   log likelihood =  -44.18954  
Iteration 9:   log likelihood = -44.189538  

Logistic regression                             Number of obs    
>  =         97
                                                LR chi2(5)       
>  =      45.59
                                                Prob > chi2      
>  =     0.0000
Log likelihood = -44.189538                     Pseudo R2        
>  =     0.3403

-----------------------------------------------------------------
> -------------
    casecont |      Coef.   Std. Err.      z    P>|z|     [95% Co
> n                                                              
>  f. Interval]
-------------+---------------------------------------------------
> -------------
        dneo |
        yes  |    3.24781   .8455337     3.84   0.000     1.59059
> 4                                                              
>      4.905025
             |
       dclox |
        yes  |   17.32151   1658.542     0.01   0.992     -3233.3
> 6                                                              
>      3268.003
             |
  dneo#dclox |
    yes#yes  |  -19.40303   1658.542    -0.01   0.991    -3270.08
> 5                                                              
>      3231.279
             |
      dcpct3 |
         50  |   1.086567   .8180393     1.33   0.184    -.516760
> 7                                                              
>      2.689894
        100  |   2.132465   .6950524     3.07   0.002     .770187
> 6                                                              
>      3.494743
             |
       _cons |  -3.580685   .9466216    -3.78   0.000    -5.43602
> 9                                                              
>     -1.725341
-----------------------------------------------------------------
> -------------

. * no interaction cov 5 only cov with dneo=no and dclox=yes with
>  cases and controls
. * the other cov with this patterns is 4 but only has one case.
. table casecont dneo dclox

------------------------------------
          | Cloxacillin used on farm
          |and Neomycin used on farm
Case -    | --- no ---    --- yes --
Control   |   no   yes      no   yes
----------+-------------------------
       no |   20    15       9    10
      yes |    2    44       3     5
------------------------------------

. table casecont dneo dclox if cov~=5

------------------------------------
          | Cloxacillin used on farm
          |and Neomycin used on farm
Case -    | --- no ---    --- yes --
Control   |   no   yes      no   yes
----------+-------------------------
       no |   20    15            10
      yes |    2    44       1     5
------------------------------------

. 
. * refitting and comparing the models 
. logit casecont dneo##dclox i.dcpct3 

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 |      Coef.   Std. Err.      z    P>|z|     [95% Co
> n                                                              
>  f. Interval]
-------------+---------------------------------------------------
> -------------
        dneo |
        yes  |    3.19238   .8361783     3.82   0.000       1.553
> 5                                                              
>      4.831259
             |
       dclox |
        yes  |   .4529145   1.026657     0.44   0.659    -1.55929
> 6                                                              
>      2.465125
             |
  dneo#dclox |
    yes#yes  |  -2.532558   1.207714    -2.10   0.036    -4.89963
> 4                                                              
>     -.1654829
             |
      dcpct3 |
         50  |   1.361002    .819178     1.66   0.097    -.244557
> 9                                                              
>      2.966561
        100  |   2.026562   .6855237     2.96   0.003     .682960
> 4                                                              
>      3.370164
             |
       _cons |  -3.531226   .9364287    -3.77   0.000    -5.36659
> 3                                                              
>      -1.69586
-----------------------------------------------------------------
> -------------

. estimate store final

. * without cov pattern 5
. logit casecont dneo##dclox i.dcpct3 if cov~=5, asis

Iteration 0:   log likelihood =  -66.98248  
Iteration 1:   log likelihood = -44.531702  
Iteration 2:   log likelihood = -44.212331  
Iteration 3:   log likelihood = -44.194711  
Iteration 4:   log likelihood = -44.190532  
Iteration 5:   log likelihood = -44.189712  
Iteration 6:   log likelihood = -44.189579  
Iteration 7:   log likelihood = -44.189547  
Iteration 8:   log likelihood =  -44.18954  
Iteration 9:   log likelihood = -44.189538  

Logistic regression                             Number of obs    
>  =         97
                                                LR chi2(5)       
>  =      45.59
                                                Prob > chi2      
>  =     0.0000
Log likelihood = -44.189538                     Pseudo R2        
>  =     0.3403

-----------------------------------------------------------------
> -------------
    casecont |      Coef.   Std. Err.      z    P>|z|     [95% Co
> n                                                              
>  f. Interval]
-------------+---------------------------------------------------
> -------------
        dneo |
        yes  |    3.24781   .8455337     3.84   0.000     1.59059
> 4                                                              
>      4.905025
             |
       dclox |
        yes  |   17.32151   1658.542     0.01   0.992     -3233.3
> 6                                                              
>      3268.003
             |
  dneo#dclox |
    yes#yes  |  -19.40303   1658.542    -0.01   0.991    -3270.08
> 5                                                              
>      3231.279
             |
      dcpct3 |
         50  |   1.086567   .8180393     1.33   0.184    -.516760
> 7                                                              
>      2.689894
        100  |   2.132465   .6950524     3.07   0.002     .770187
> 6                                                              
>      3.494743
             |
       _cons |  -3.580685   .9466216    -3.78   0.000    -5.43602
> 9                                                              
>     -1.725341
-----------------------------------------------------------------
> -------------

. estimates store wocov5

. * without cov pattern 6
. logit casecont dneo##dclox i.dcpct3 if cov~=6

Iteration 0:   log likelihood = -67.188877  
Iteration 1:   log likelihood = -44.268414  
Iteration 2:   log likelihood = -43.951293  
Iteration 3:   log likelihood = -43.949794  
Iteration 4:   log likelihood = -43.949794  

Logistic regression                             Number of obs    
>  =         97
                                                LR chi2(5)       
>  =      46.48
                                                Prob > chi2      
>  =     0.0000
Log likelihood = -43.949794                     Pseudo R2        
>  =     0.3459

-----------------------------------------------------------------
> -------------
    casecont |      Coef.   Std. Err.      z    P>|z|     [95% Co
> n                                                              
>  f. Interval]
-------------+---------------------------------------------------
> -------------
        dneo |
        yes  |   3.639127     .99651     3.65   0.000     1.68600
> 3                                                              
>      5.592251
             |
       dclox |
        yes  |   .8080203   1.132313     0.71   0.475    -1.41127
> 2                                                              
>      3.027313
             |
  dneo#dclox |
    yes#yes  |  -3.018298    1.34906    -2.24   0.025    -5.66240
> 8                                                              
>     -.3741881
             |
      dcpct3 |
         50  |   .1199465   1.407715     0.09   0.932    -2.63912
> 4                                                              
>      2.879017
        100  |   .8134231   1.299859     0.63   0.531    -1.73425
> 5                                                              
>      3.361101
             |
       _cons |  -2.671599   1.073272    -2.49   0.013    -4.77517
> 4                                                              
>     -.5680239
-----------------------------------------------------------------
> -------------

. estimates store wocov6

. * without cov pattern 8
. logit casecont dneo##dclox i.dcpct3 if cov~=8

Iteration 0:   log likelihood = -42.760501  
Iteration 1:   log likelihood = -36.670668  
Iteration 2:   log likelihood = -36.284326  
Iteration 3:   log likelihood = -36.280224  
Iteration 4:   log likelihood = -36.280224  

Logistic regression                             Number of obs    
>  =         70
                                                LR chi2(5)       
>  =      12.96
                                                Prob > chi2      
>  =     0.0238
Log likelihood = -36.280224                     Pseudo R2        
>  =     0.1515

-----------------------------------------------------------------
> -------------
    casecont |      Coef.   Std. Err.      z    P>|z|     [95% Co
> n                                                              
>  f. Interval]
-------------+---------------------------------------------------
> -------------
        dneo |
        yes  |   2.518415   .9964233     2.53   0.011     .565461
> 1                                                              
>      4.471369
             |
       dclox |
        yes  |   .7045019   1.065468     0.66   0.508    -1.38377
> 6                                                              
>       2.79278
             |
  dneo#dclox |
    yes#yes  |   -2.05333   1.255048    -1.64   0.102     -4.5131
> 8                                                              
>      .4065195
             |
      dcpct3 |
         50  |   1.173495   .7931855     1.48   0.139    -.381119
> 5                                                              
>       2.72811
        100  |   1.168346   1.025769     1.14   0.255     -.84212
> 5                                                              
>      3.178817
             |
       _cons |   -2.97189   .9807858    -3.03   0.002    -4.89419
> 4                                                              
>     -1.049585
-----------------------------------------------------------------
> -------------

. estimates store wocov8

. estimates table final wocov5 wocov6 wocov8 , b(%5.3f)  stats(N)
>  star( .05 .01 .001)

-----------------------------------------------------
    Variable |   final        wocov5       wocov6    
-------------+---------------------------------------
        dneo |
        yes  |   3.192***     3.248***     3.639***  
             |
       dclox |
        yes  |   0.453       17.322        0.808     
             |
  dneo#dclox |
    yes#yes  |  -2.533*     -19.403       -3.018*    
             |
      dcpct3 |
         50  |   1.361        1.087        0.120     
        100  |   2.027**      2.132**      0.813     
             |
       _cons |  -3.531***    -3.581***    -2.672*    
-------------+---------------------------------------
           N |     108           97           97     
-----------------------------------------------------
                legend: * p<.05; ** p<.01; *** p<.001


---------------------------
    Variable |   wocov8    
-------------+-------------
             |
        dneo |
        yes  |   2.518*    
             |
       dclox |
        yes  |   0.705     
             |
  dneo#dclox |
    yes#yes  |  -2.053     
             |
      dcpct3 |
         50  |   1.173     
        100  |   1.168     
             |
       _cons |  -2.972**   
-------------+-------------
           N |      70     
---------------------------
legend: * p<.05; ** p<.01; *** p<.001

. 
. *Predictive ability of the model
. * sensitivity and specificty of logistic model
. logit casecont dneo##dclox i.dcpct3 

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 |      Coef.   Std. Err.      z    P>|z|     [95% Co
> n                                                              
>  f. Interval]
-------------+---------------------------------------------------
> -------------
        dneo |
        yes  |    3.19238   .8361783     3.82   0.000       1.553
> 5                                                              
>      4.831259
             |
       dclox |
        yes  |   .4529145   1.026657     0.44   0.659    -1.55929
> 6                                                              
>      2.465125
             |
  dneo#dclox |
    yes#yes  |  -2.532558   1.207714    -2.10   0.036    -4.89963
> 4                                                              
>     -.1654829
             |
      dcpct3 |
         50  |   1.361002    .819178     1.66   0.097    -.244557
> 9                                                              
>      2.966561
        100  |   2.026562   .6855237     2.96   0.003     .682960
> 4                                                              
>      3.370164
             |
       _cons |  -3.531226   .9364287    -3.77   0.000    -5.36659
> 3                                                              
>      -1.69586
-----------------------------------------------------------------
> -------------

. estat class

Logistic model for casecont

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        40             8  |         48
     -     |        14            46  |         60
-----------+--------------------------+-----------
   Total   |        54            54  |        108

Classified + if predicted Pr(D) >= .5
True D defined as casecont != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   74.07%
Specificity                     Pr( -|~D)   85.19%
Positive predictive value       Pr( D| +)   83.33%
Negative predictive value       Pr(~D| -)   76.67%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   14.81%
False - rate for true D         Pr( -| D)   25.93%
False + rate for classified +   Pr(~D| +)   16.67%
False - rate for classified -   Pr( D| -)   23.33%
--------------------------------------------------
Correctly classified                        79.63%
--------------------------------------------------

. * two graph ROC
. lsens, lpattern(solid dash)

. * changing the cutpoint  and producing an ROC curve and LR tabl
> e
. egen pv_cat=cut(pv), at(0(.05)1) 

. roctab casecont pv_cat, graph sum detail 

Detailed report of sensitivity and specificity
-----------------------------------------------------------------
> -------------
                                           Correctly
Cutpoint      Sensitivity   Specificity   Classified          LR+
>           LR-
-----------------------------------------------------------------
> -------------
( >= 0 )          100.00%         0.00%       50.00%       1.0000
>      
( >= .05 )         98.15%        20.37%       59.26%       1.2326
>        0.0909
( >= .1 )          98.15%        22.22%       60.19%       1.2619
>        0.0833
( >= .15 )         98.15%        25.93%       62.04%       1.3250
>        0.0714
( >= .25 )         94.44%        38.89%       66.67%       1.5455
>        0.1429
( >= .4 )          88.89%        62.96%       75.93%       2.4000
>        0.1765
( >= .7 )          74.07%        85.19%       79.63%       5.0000
>        0.3043
( >= .8 )          61.11%        90.74%       75.93%       6.6000
>        0.4286
( >  .8 )           0.00%       100.00%       50.00%             
>        1.0000
-----------------------------------------------------------------
> -------------


                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
           108     0.8488       0.0370        0.77621     0.92132

. estat class, cut(0.25) // no in the notes - change cutpoint to 
> 0.25 

Logistic model for casecont

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        51            33  |         84
     -     |         3            21  |         24
-----------+--------------------------+-----------
   Total   |        54            54  |        108

Classified + if predicted Pr(D) >= .25
True D defined as casecont != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   94.44%
Specificity                     Pr( -|~D)   38.89%
Positive predictive value       Pr( D| +)   60.71%
Negative predictive value       Pr(~D| -)   87.50%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   61.11%
False - rate for true D         Pr( -| D)    5.56%
False + rate for classified +   Pr(~D| +)   39.29%
False - rate for classified -   Pr( D| -)   12.50%
--------------------------------------------------
Correctly classified                        66.67%
--------------------------------------------------

.                                           // increase Se and de
> crease Sp
. *ROC plot and AUC after command
. lroc

Logistic model for casecont

number of observations =      108
area under ROC curve   =   0.8460

. 
end of do-file

. log close
      name:  <unnamed>
       log:  C:\vhm812-data\l5a_log_reg_dx2017.txt
  log type:  text
 closed on:   5 Feb 2018, 15:12:29
-----------------------------------------------------------------