Hello statalist,

I am involved in a project looking at net use in preventing malaria. I have a dataset containing the computed probabilities of certain events as described in the dataset below. They are in continuous form. I wish to calculate the conditional probability of having fever given the rate of net use for the month (varname netuse), the probabilty of having the house sprayed (varname spray) and the prevalence of fever for the month (fever). How can I achieve this?


Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input int year float month byte district float(netuse fever spray)
2016  5  9   .2726605   .3488717   .6880603
2016  4 10   .7239472   .2668857   .9794578
2016  2  9   .7955464   .1366463   .6701937
2016  2  9   .8925074 .028556867   .5948808
2016  3  9   .7078791   .8689333   .7970893
2016  8  9    .365269   .3508549   .7835853
2016  1  9   .9310499  .07110509   .6546342
2016  7  9   .6216809  .32336795  .09688907
2016  4  9   .8004354   .5551032   .6885059
2016  5  9   .4798372    .875991    .872496
2016 10  9  .14294797  .20470947  .52963525
2016  4  9   .8343448   .8927587   .8302209
2016 11 31   .3431251   .5844658   .9339853
2016  2 31   .5906867   .3697791   .1749891
2016  7 31     .32953   .8506309   .5536171
2016  2 31   .6996295   .3913819   .5346152
2016  4 31   .8142969  .11966132   .7767794
2016  3 31   .3429726   .7542434   .1288747
2016 11 55  .10796808   .6950234  .27751842
2016 10 55   .9671743   .6866152   .4242016
2016  8 45   .1285523   .9319346  .13590056
2016  6 45   .7578536   .4548882   .3325624
2016  2 13  .25002995   .0674011   .4675523
2016  9 61   .9269138   .3379889  .51608807
2016  4 61   .7118431   .9748848  .06694305
2016  7 61  .17967154   .7264384  .07229638
2016  9 61  .45065185  .04541512   .6817465
2016  5 61   .1946068   .7459667  .08804953
2016  6 61   .7135741   .4961259  .13270818
2016  8 61  .24531136   .7167162   .8745816
2016  7 23   .7672456    .859742   .2468877
2016  9 23   .3653557  .13407555    .043255
2016 11 23  .27069142  .48844185   .3764437
2016 10 23   .9911318   .8712187   .7677861
2016  3 23   .6851298   .7664683   .7551366
2016  4 23   .5027668  .25125554   .4476188
2016  9 72   .6903818  .16636477   .4087105
2016  4 72   .8636012   .7437958  .29777426
2016  6 72   .0404633   .9805113   .6794177
2016 11  3   .1842219   .7295772   .7124024
2016 11 16   .4198807   .9011049  .56622654
2016  4 16   .6475499  .26436493   .1778325
2016  9 16   .9103145   .8856509  .11399896
2016  7 16   .6809221    .882112   .5955869
2016  9 16   .8568827    .748933   .6251604
2016  6 16  .06420179   .9196262    .634899
2016  8 16   .8390664   .6934533   .9944572
2016  5 16   .6208202   .2154026   .7497677
2016  7 16   .4041756   .8285888   .1736788
2016 11 52   .9786366  .04421536   .6107705
2016  9 52  .36276805   .8630378   .5754215
2016  6 52  .31382445   .3526046   .3678161
2016  6 76   .6380712   .7720399   .3005246
2016  7 76  .18699375   .5861199 .007538023
2016  5 76  .50534767   .3227766   .6701369
2016 11 76   .5276305  .17293066   .4241406
2016  7 76   .7853414   .8053644   .9537622
2016  1 76   .4717338   .3060019  .08674778
2016  7 76   .2299842  .21909967   .8949648
2016  8 76   .7976828    .724731   .5890286
2016 10 76  .16493952   .6964867   .4005832
2016  8 76    .932945   .9119344   .6654902
2016  2 76   .3999315   .6795634   .4198386
2016 12 76   .9881987   .3549416   .7472054
2016 10 76   .9287856     .73897   .7190143
2016  7 76   .6640378  .18740167   .8464647
2016  8 76 .036803816   .3146128   .7908313
2016  4 76   .3336498   .1375693   .1900222
2016  8 76   .7824295   .6537739   .3869604
2016  8 76  .01700492  .27013195  .23871335
2016  6 76   .2278204   .8998394   .3447002
2016 10 76  .57824653   .5734232   .7795682
2016  9 76   .7533595  .11147037   .7484396
2016  9 76   .8570072   .4145227  .23037836
2016  5 76   .9322746 .003052204  .16770323
2016  5 76    .324447   .6659978   .9180508
2016  9 76   .1637711   .3462876   .3138996
2016 12  2    .958201   .0780235   .9019141
2016 10 17   .6008608  .12758136  .07740517
2016 11 17   .9733476   .2297006   .6341382
2016  7 17   .2363827   .3295547   .8147295
2016 11 17   .6764786   .4144089   .8788922
2016  4 17  .14591032 .036084738  .02599352
2016 10 17  .29664016  .08438109     .17993
2016  2 17   .8219558 .009876247  .57788956
2016 11 17   .3213928   .3200437   .4081415
2016  5 17   .4164997 .005196966   .6155495
2016  4 17  .02369639  .22754347  .17457695
2016 10 17   .3125404    .851468   .3617646
2016  4 74   .9322619   .9820066   .1338996
2016  9 74   .0502046 .032479186 .001363096
2016  3 74   .6221892   .9874847  .25710005
2016  3 74   .6189114    .894106   .6517417
2016  6 44   .9028944   .9684734   .9252081
2016 11 44   .3830579  .23922028   .8233367
2016  1 44  .35137045   .6927336   .9229402
2016  3 74   .6978495   .4884359   .7480426
2016  7 74   .7828125   .4376452  .52141476
2016  9 74   .8312564   .5858005   .4022151
2016  7 74   .8498105   .3787092   .8681989
end
label values district district_id
label def district_id 2 "Bambey", modify
label def district_id 3 "Bignona", modify
label def district_id 9 "Dakar-nord", modify
label def district_id 10 "Dakar-ouest", modify
label def district_id 13 "Diameniadio", modify
label def district_id 16 "Diouloulou", modify
label def district_id 17 "Diourbel", modify
label def district_id 23 "Guédiawaye", modify
label def district_id 31 "Keur Massar", modify
label def district_id 44 "Mbacké", modify
label def district_id 45 "Mbao", modify
label def district_id 52 "Oussouye", modify
label def district_id 55 "Pikine", modify
label def district_id 61 "Rufisque", modify
label def district_id 72 "Thionck-Essyl", modify
label def district_id 74 "Touba", modify
label def district_id 76 "Ziguinchor", modify