% usefile C:\monographregression\computercode\prg\bayesx\zambia_casestudy.txt % HINT: % The program assumes that all files are stored in % C:\monographregression\computercode % change directory if code is located elsewhere % To store results you have to create a subfolder `results' (or any other folder defined in % the outfile command, see below) % To execute this program copy the usefile command above in the command window of BayesX % Open a log file logopen using C:\monographregression\computercode\results\zambia_casestudy.log % Create a dataset object and read the data dataset d d.infile using C:\monographregression\computercode\data\bayesx\zambia_height92.raw % Delete extreme observations d.drop if m_bmi>40 d.drop if c_breastf>c_age d.drop if c_breastf>30 % Generate a quadratic polynomial for the mother's BMI d.generate m_bmi2 = m_bmi*m_bmi % Generate dummy variables for the mother's education d.generate m_education1 = 1*(m_education=1) d.generate m_education3 = 1*(m_education=3) d.generate m_education4 = 1*(m_education=4) % Generate dummy variables for the regions d.generate region1 = 1*(region=1) d.generate region3 = 1*(region=3) d.generate region4 = 1*(region=4) d.generate region5 = 1*(region=5) d.generate region6 = 1*(region=6) d.generate region7 = 1*(region=7) d.generate region8 = 1*(region=8) d.generate region9 = 1*(region=9) % Spline-basis for the age effect: d.generate c_age_spline = c_age*(c_age>23.5) % Create a map object and read the data map m m.infile using C:\monographregression\computercode\data\bayesx\zambia.txt % Create a dataset object for the knots and read the data dataset kn kn.infile using C:\monographregression\computercode\data\bayesx\MIN_f_c_breastf_c_age_kriging_knots.raw % Create a regression object remlreg r % Set a new delimiter delimiter=; % Model MIB r.outfile = C:\monographregression\computercode\results\MIB; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi(psplinerw2) + c_breastf*c_age(kriging, knotdata=kn) + district(spatial, map=m) , family=gaussian using d; % Model MIN r.outfile = C:\monographregression\computercode\results\MIN; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth(psplinerw2) + m_height(psplinerw2) + m_bmi(psplinerw2) + c_breastf*c_age(kriging, knotdata=kn) + district(spatial, map=m) , family=gaussian using d; % Model MIP r.outfile = C:\monographregression\computercode\results\MIP; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi + c_breastf*c_age(kriging, knotdata=kn) + district(spatial, map=m) , family=gaussian using d; % Model MIQ r.outfile = C:\monographregression\computercode\results\MIQ; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi + m_bmi2 + c_breastf*c_age(kriging, knotdata=kn) + district(spatial, map=m) , family=gaussian using d; % Model RIB r.outfile = C:\monographregression\computercode\results\RIB; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi(psplinerw2) + c_breastf*c_age(kriging, knotdata=kn) + district(random) , family=gaussian using d; % Model RIN r.outfile = C:\monographregression\computercode\results\RIN; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth(psplinerw2) + m_height(psplinerw2) + m_bmi(psplinerw2) + c_breastf*c_age(kriging, knotdata=kn) + district(random) , family=gaussian using d; % Model RIQ r.outfile = C:\monographregression\computercode\results\RIQ; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi + m_bmi2 + c_breastf*c_age(kriging, knotdata=kn) + district(random) , family=gaussian using d; % Model RIP r.outfile = C:\monographregression\computercode\results\RIP; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi + c_breastf*c_age(kriging, knotdata=kn) + district(random) , family=gaussian using d; % Model PIN r.outfile = C:\monographregression\computercode\results\PIN; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth(psplinerw2) + m_height(psplinerw2) + m_bmi(psplinerw2) + c_breastf*c_age(kriging, knotdata=kn) + region1 + region3 + region4 + region5 + region6 + region7 + region8 + region9 , family=gaussian using d; % Model PIB r.outfile = C:\monographregression\computercode\results\PIB; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi(psplinerw2) + c_breastf*c_age(kriging, knotdata=kn) + region1 + region3 + region4 + region5 + region6 + region7 + region8 + region9 , family=gaussian using d; % Model PIQ r.outfile = C:\monographregression\computercode\results\PIQ; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi + m_bmi2 + c_breastf*c_age(kriging, knotdata=kn) + region1 + region3 + region4 + region5 + region6 + region7 + region8 + region9 , family=gaussian using d; % Model PIP r.outfile = C:\monographregression\computercode\results\PIP; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi + c_breastf*c_age(kriging, knotdata=kn) + region1 + region3 + region4 + region5 + region6 + region7 + region8 + region9 , family=gaussian using d; % Model MNQ r.outfile = C:\monographregression\computercode\results\MNQ; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi + m_bmi2 + c_age(psplinerw2) + district(spatial, map=m) , family=gaussian using d; % Model MNN r.outfile = C:\monographregression\computercode\results\MNN; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth(psplinerw2) + m_height(psplinerw2) + m_bmi(psplinerw2) + c_age(psplinerw2) + district(spatial, map=m) , family=gaussian using d; % Model MNB r.outfile = C:\monographregression\computercode\results\MNB; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi(psplinerw2) + c_age(psplinerw2) + district(spatial, map=m) , family=gaussian using d; % Model MNP r.outfile = C:\monographregression\computercode\results\MNP; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi + c_age(psplinerw2) + district(spatial, map=m) , family=gaussian using d; % Model RNQ r.outfile = C:\monographregression\computercode\results\RNQ; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi + m_bmi2 + c_age(psplinerw2) + district(random) , family=gaussian using d; % Model RNB r.outfile = C:\monographregression\computercode\results\RNB; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi(psplinerw2) + c_age(psplinerw2) + district(random) , family=gaussian using d; % Model RNN r.outfile = C:\monographregression\computercode\results\RNN; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth(psplinerw2) + m_height(psplinerw2) + m_bmi(psplinerw2) + c_age(psplinerw2) + district(random) , family=gaussian using d; % Model RNP r.outfile = C:\monographregression\computercode\results\RNP; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi + c_age(psplinerw2) + district(random) , family=gaussian using d; % Model PNQ r.outfile = C:\monographregression\computercode\results\PNQ; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi + m_bmi2 + c_age(psplinerw2) + region1 + region3 + region4 + region5 + region6 + region7 + region8 + region9 , family=gaussian using d; % Model PNN r.outfile = C:\monographregression\computercode\results\PNN; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth(psplinerw2) + m_height(psplinerw2) + m_bmi(psplinerw2) + c_age(psplinerw2) + region1 + region3 + region4 + region5 + region6 + region7 + region8 + region9 , family=gaussian using d; % Model PNB r.outfile = C:\monographregression\computercode\results\PNB; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi(psplinerw2) + c_age(psplinerw2) + region1 + region3 + region4 + region5 + region6 + region7 + region8 + region9 , family=gaussian using d; % Model PNP r.outfile = C:\monographregression\computercode\results\PNP; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi + c_age(psplinerw2) + region1 + region3 + region4 + region5 + region6 + region7 + region8 + region9 , family=gaussian using d; % Parametric model r.outfile = C:\monographregression\computercode\results\param; r.regress zscore = c_gender + m_education1 + m_education3 + m_education4 + m_work + m_agebirth + m_height + m_bmi + m_bmi2 + c_age + c_age_spline + region1 + region3 + region4 + region5 + region6 + region7 + region8 + region9 , family=gaussian using d; % Close the log file logclose;