# Convolve with HRF df = convolve_hrf(df, dg(), asbold) # Orth select regressors to_orth = [['box', bold] for bold in asbold if bold != 'box'] for orth in to_orth: df[orth[1]+'_o'] = orthogonalize(df, orth)[orth[1]] # Do the regressions n_results = {} for model_name, model, test, hypoth in zip(*model_configs): for bold_name in asbold: l = df.shape[0] noi, prng = white(l, prng=prng) df['bold'] = create_bold([df[bold_name].values], None, noi) smo = smf.ols(model, data=df).fit() print(smo.summary2()) stato = None if test == 't': stato = smo.t_test(hypoth) elif test == 'F': stato = smo.f_test(hypoth) elif test is not None: raise ValueError("Unknown test") savedf = None if args.save_behave: savedf = df
# creates the BOLD. The second creates # the predictors. for alpha_bold in alphas: # Create all bold options df_bold, _ = reinforce.rescorla_wagner(trials, acc, p, alpha=alpha_bold, prng=prng) # Iter options for bold_name in asbold: l = df_bold.shape[0] noi, prng = white(l, prng=prng) df_bold['bold'] = create_bold([df_bold[bold_name].values], dg(), noi) # Create predictor for alpha_pred in alphas: df_pred, _ = reinforce.rescorla_wagner(trials, acc, p, alpha=alpha_pred, prng=prng) df_pred = convolve_hrf(df_pred, dg(), asbold) # Orth select predictors to_orth = [['box', too] for too in asbold if too != 'box'] for orth in to_orth: df_pred[orth[1] + '_o'] = orthogonalize(df_pred,
# creates the BOLD. The second creates # the predictors. for alpha_bold in alphas: # Create all bold options df_bold, _ = reinforce.rescorla_wagner( trials, acc, p, alpha=alpha_bold, prng=prng ) # Iter options for bold_name in asbold: l = df_bold.shape[0] noi, prng = white(l, prng=prng) df_bold['bold'] = create_bold( [df_bold[bold_name].values], dg(), noi ) # Create predictor for alpha_pred in alphas: df_pred, _ = reinforce.rescorla_wagner( trials, acc, p, alpha=alpha_pred, prng=prng ) df_pred = convolve_hrf(df_pred, dg(), asbold) # Orth select predictors to_orth = [['box', too] for too in asbold if too != 'box'] for orth in to_orth: df_pred[orth[1]+'_o'] = orthogonalize(
df = convolve_hrf(df, dg(), asbold) # Orth select regressors to_orth = [['box', too] for too in asbold if too != 'box'] for orth in to_orth: df[orth[1]+'_o'] = orthogonalize(df, orth)[orth[1]] # Do the regressions n_results = {} for model_name, model, test, hypoth in zip(*model_configs): for bold_name in asbold: l = df.shape[0] noi, prng = white(l, prng=prng) # Make bold df['bold'] = create_bold([df[bold_name].values], None, noi) # Regress smo = smf.ols(model, data=df).fit() print(smo.summary2()) # Hypoth test stato = None if test == 't': stato = smo.t_test(hypoth) elif test == 'F': stato = smo.f_test(hypoth) elif test is not None: raise ValueError("Unknown test") # Reformat