Closed djwooten closed 4 years ago
## use the github test dataset "sample_data_1.csv" ## https://github.com/djwooten/synergy/tree/master/datasets ## all concs x 10 model.fit(df['drug1.conc']*10, df['drug2.conc']*10, df['effect'], bootstrap_iterations=100) print(model.summary(confidence_interval=95)) # result # beta 0.59 (0.44,0.71) (>0) synergistic # alpha12 0.37 (0.32,0.49) (<1) antagonistic # alpha21 2.00 (1.55,2.46) (>1) synergistic # gamma12 3.29 (1.97,93.37) (>1) synergistic ## all concs x1000 model.fit(df['drug1.conc']*1000, df['drug2.conc']*1000, df['effect'], bootstrap_iterations=100) print(model.summary(confidence_interval=95)) # result # beta 0.50 (0.31,0.74) (>0) synergistic # alpha21 1.89 (1.37,2.75) (>1) synergistic ## only conc2 x1000 model.fit(df['drug1.conc'], df['drug2.conc']*1000, df['effect'], bootstrap_iterations=100) print(model.summary(confidence_interval=95)) # result # beta 0.39 (0.24,0.53) (>0) synergistic # alpha12 0.57 (0.49,0.75) (<1) antagonistic # alpha21 2.21 (1.76,2.56) (>1) synergistic # gamma12 3.11 (2.28,5.39) (>1) synergistic # gamma21 0.87 (0.77,0.97) (<1) antagonistic
Changed fitting procedure to fit r1 and r2, which are parameters containing dose-scale information.