import pandas as pd import numpy as np import biogeme.database as db import biogeme.biogeme as bio from biogeme.models import piecewise import biogeme.loglikelihood as ll pandas = pd.read_table("optima.dat") database = db.Database("optima",pandas) from headers import * exclude = (Choice == -1.0) database.remove(exclude) # Piecewise linear definition of income ScaledIncome = DefineVariable('ScaledIncome',\ CalculatedIncome / 1000,database) thresholds = [4,6,8,10] ContIncome = piecewise(ScaledIncome,thresholds) ContIncome_0_4000 = ContIncome[0] ContIncome_4000_6000 = ContIncome[1] ContIncome_6000_8000 = ContIncome[2] ContIncome_8000_10000 = ContIncome[3] ContIncome_10000_more = ContIncome[4] age_65_more = DefineVariable('age_65_more',age >= Numeric(65),database) moreThanOneCar = DefineVariable('moreThanOneCar',NbCar > 1,database) moreThanOneBike = DefineVariable('moreThanOneBike',NbBicy > 1,database) individualHouse = DefineVariable('individualHouse',\ HouseType == 1,database) male = DefineVariable('male',Gender == 1,database) haveChildren = DefineVariable('haveChildren',\ ((FamilSitu == 3)+(FamilSitu == 4)) > 0,database) haveGA = DefineVariable('haveGA',GenAbST == 1,database) highEducation = DefineVariable('highEducation', Education >= 6,database) ### Coefficients coef_intercept = Beta('coef_intercept',0.0,None,None,0) coef_age_65_more = Beta('coef_age_65_more',0.0,None,None,0) coef_age_unknown = Beta('coef_age_unknown',0.0,None,None,0) coef_haveGA = Beta('coef_haveGA',0.0,None,None,0) coef_ContIncome_0_4000 = \ Beta('coef_ContIncome_0_4000',0.0,None,None,0) coef_ContIncome_4000_6000 = \ Beta('coef_ContIncome_4000_6000',0.0,None,None,0) coef_ContIncome_6000_8000 = \ Beta('coef_ContIncome_6000_8000',0.0,None,None,0) coef_ContIncome_8000_10000 = \ Beta('coef_ContIncome_8000_10000',0.0,None,None,0) coef_ContIncome_10000_more = \ Beta('coef_ContIncome_10000_more',0.0,None,None,0) coef_moreThanOneCar = \ Beta('coef_moreThanOneCar',0.0,None,None,0) coef_moreThanOneBike = \ Beta('coef_moreThanOneBike',0.0,None,None,0) coef_individualHouse = \ Beta('coef_individualHouse',0.0,None,None,0) coef_male = Beta('coef_male',0.0,None,None,0) coef_haveChildren = Beta('coef_haveChildren',0.0,None,None,0) coef_highEducation = Beta('coef_highEducation',0.0,None,None,0) ### Latent variable: structural equation # Note that the expression must be on a single line. In order to # write it across several lines, each line must terminate with # the \ symbol CARLOVERS = \ coef_intercept +\ coef_age_65_more * age_65_more +\ coef_ContIncome_0_4000 * ContIncome_0_4000 +\ coef_ContIncome_4000_6000 * ContIncome_4000_6000 +\ coef_ContIncome_6000_8000 * ContIncome_6000_8000 +\ coef_ContIncome_8000_10000 * ContIncome_8000_10000 +\ coef_ContIncome_10000_more * ContIncome_10000_more +\ coef_moreThanOneCar * moreThanOneCar +\ coef_moreThanOneBike * moreThanOneBike +\ coef_individualHouse * individualHouse +\ coef_male * male +\ coef_haveChildren * haveChildren +\ coef_haveGA * haveGA +\ coef_highEducation * highEducation sigma_s = Beta('sigma_s',1,0.001,None,1) ### Measurement equations INTER_Envir01 = Beta('INTER_Envir01',0,None,None,1) INTER_Envir02 = Beta('INTER_Envir02',0,None,None,0) INTER_Envir03 = Beta('INTER_Envir03',0,None,None,0) INTER_Mobil11 = Beta('INTER_Mobil11',0,None,None,0) INTER_Mobil14 = Beta('INTER_Mobil14',0,None,None,0) INTER_Mobil16 = Beta('INTER_Mobil16',0,None,None,0) INTER_Mobil17 = Beta('INTER_Mobil17',0,None,None,0) B_Envir01_F1 = Beta('B_Envir01_F1',-1,None,None,1) B_Envir02_F1 = Beta('B_Envir02_F1',-1,None,None,0) B_Envir03_F1 = Beta('B_Envir03_F1',1,None,None,0) B_Mobil11_F1 = Beta('B_Mobil11_F1',1,None,None,0) B_Mobil14_F1 = Beta('B_Mobil14_F1',1,None,None,0) B_Mobil16_F1 = Beta('B_Mobil16_F1',1,None,None,0) B_Mobil17_F1 = Beta('B_Mobil17_F1',1,None,None,0) MODEL_Envir01 = INTER_Envir01 + B_Envir01_F1 * CARLOVERS MODEL_Envir02 = INTER_Envir02 + B_Envir02_F1 * CARLOVERS MODEL_Envir03 = INTER_Envir03 + B_Envir03_F1 * CARLOVERS MODEL_Mobil11 = INTER_Mobil11 + B_Mobil11_F1 * CARLOVERS MODEL_Mobil14 = INTER_Mobil14 + B_Mobil14_F1 * CARLOVERS MODEL_Mobil16 = INTER_Mobil16 + B_Mobil16_F1 * CARLOVERS MODEL_Mobil17 = INTER_Mobil17 + B_Mobil17_F1 * CARLOVERS SIGMA_STAR_Envir01 = Beta('SIGMA_STAR_Envir01',10,0.001,None,0) SIGMA_STAR_Envir02 = Beta('SIGMA_STAR_Envir02',10,0.001,None,0) SIGMA_STAR_Envir03 = Beta('SIGMA_STAR_Envir03',10,0.001,None,0) SIGMA_STAR_Mobil11 = Beta('SIGMA_STAR_Mobil11',10,0.001,None,0) SIGMA_STAR_Mobil14 = Beta('SIGMA_STAR_Mobil14',10,0.001,None,0) SIGMA_STAR_Mobil16 = Beta('SIGMA_STAR_Mobil16',10,0.001,None,0) SIGMA_STAR_Mobil17 = Beta('SIGMA_STAR_Mobil17',10,0.001,None,0) F = {} F['Envir01'] = Elem({0:0, \ 1:ll.loglikelihoodregression(Envir01,MODEL_Envir01,SIGMA_STAR_Envir01)},\ (Envir01 > 0)*(Envir01 < 6)) F['Envir02'] = Elem({0:0, \ 1:ll.loglikelihoodregression(Envir02,MODEL_Envir02,SIGMA_STAR_Envir02)},\ (Envir02 > 0)*(Envir02 < 6)) F['Envir03'] = Elem({0:0, \ 1:ll.loglikelihoodregression(Envir03,MODEL_Envir03,SIGMA_STAR_Envir03)},\ (Envir03 > 0)*(Envir03 < 6)) F['Mobil11'] = Elem({0:0, \ 1:ll.loglikelihoodregression(Mobil11,MODEL_Mobil11,SIGMA_STAR_Mobil11)},\ (Mobil11 > 0)*(Mobil11 < 6)) F['Mobil14'] = Elem({0:0, \ 1:ll.loglikelihoodregression(Mobil14,MODEL_Mobil14,SIGMA_STAR_Mobil14)},\ (Mobil14 > 0)*(Mobil14 < 6)) F['Mobil16'] = Elem({0:0, \ 1:ll.loglikelihoodregression(Mobil16,MODEL_Mobil16,SIGMA_STAR_Mobil16)},\ (Mobil16 > 0)*(Mobil16 < 6)) F['Mobil17'] = Elem({0:0, \ 1:ll.loglikelihoodregression(Mobil17,MODEL_Mobil17,SIGMA_STAR_Mobil17)},\ (Mobil17 > 0)*(Mobil17 < 6)) loglike = bioMultSum(F) biogeme = bio.BIOGEME(database,loglike) biogeme.modelName = "01oneLatentRegression" results = biogeme.estimate() print(f"Estimated betas: {len(results.data.betaValues)}") print(f"final log likelihood: {results.data.logLike:.3f}") print(f"Output file: {results.data.htmlFileName}") results.writeLaTeX() print(f"LaTeX file: {results.data.latexFileName}")