stan_modelling_theoretic.py

    perfComp(
        dg, lambda x: decider(x),
        "Fluctuation of failure rate estimates across iterations\n" +
        "Bernoulli + independent decisions, without unobservables",
        figure_path + "sl_bernoulli_independent_without_Z",
        model_type="lr", 
        fit_with_Z=False
    )

if which == 3:
    print("\nWith unobservables (Bernoullian outcome + independent decisions)")
    
    print("Decision-maker in the data and model: random and y ~ x + z")

    dg = lambda: bernoulliDGWithUnobservables(N_total=N_sim)

    decider = lambda x: randomDecider(x, nJudges_M=M_sim, use_acceptance_rates=True)

    perfComp(
        dg, lambda x: decider(x),
        "Fluctuation of failure rate estimates across iterations\n" +
        "Bernoulli + independent decisions, without unobservables",
        figure_path + "sl_bernoulli_independent_without_Z",
        model_type="lr", 
        fit_with_Z=True
    )

if which == 4:
    print("\nWith unobservables (Bernoullian outcome + independent decisions)")
    
    print("Decision-maker in the data and model: y ~ x and random")

    dg = lambda: bernoulliDGWithUnobservables(N_total=N_sim)

    decider = lambda x: quantileDecider(
        x, featureX_col="X", featureZ_col=None, nJudges_M=M_sim, beta_X=1, beta_Z=1)

    perfComp(
        dg, lambda x: decider(x),
        "Fluctuation of failure rate estimates across iterations\n" +
        "Bernoulli + independent decisions, without unobservables",
        figure_path + "sl_bernoulli_independent_without_Z",
        model_type="fully_random", 
        fit_with_Z=False
    )

if which == 5:
    print("\nWith unobservables (Bernoullian outcome + independent decisions)")
    
    print("Decision-maker in the data and model: y ~ x and y ~ x")

    dg = lambda: bernoulliDGWithUnobservables(N_total=N_sim)

    decider = lambda x: quantileDecider(
        x, featureX_col="X", featureZ_col=None, nJudges_M=M_sim, beta_X=1, beta_Z=1)

    perfComp(
        dg, lambda x: decider(x),
        "Fluctuation of failure rate estimates across iterations\n" +
        "Bernoulli + independent decisions, without unobservables",
        figure_path + "sl_bernoulli_independent_without_Z",
        model_type="lr", 
        fit_with_Z=False
    )
    
if which == 6:
    print("\nWith unobservables (Bernoullian outcome + independent decisions)")
    
    print("Decision-maker in the data and model: y ~ x and y ~ x + z")

    dg = lambda: bernoulliDGWithUnobservables(N_total=N_sim)

    decider = lambda x: quantileDecider(
        x, featureX_col="X", featureZ_col=None, nJudges_M=M_sim, beta_X=1, beta_Z=1)

    perfComp(
        dg, lambda x: decider(x),
        "Fluctuation of failure rate estimates across iterations\n" +
        "Bernoulli + independent decisions, without unobservables",
        figure_path + "sl_bernoulli_independent_without_Z",
        model_type="lr", 
        fit_with_Z=True
    )
    
if which == 7:
    print("\nWith unobservables (Bernoullian outcome + independent decisions)")
    
    print("Decision-maker in the data and model: y ~ x + z and random")

    dg = lambda: bernoulliDGWithUnobservables(N_total=N_sim)

    decider = lambda x: quantileDecider(
        x, featureX_col="X", featureZ_col='Z', nJudges_M=M_sim, beta_X=1, beta_Z=1)

    perfComp(
        dg, lambda x: decider(x),
        "Fluctuation of failure rate estimates across iterations\n" +
        "Bernoulli + independent decisions, without unobservables",
        figure_path + "sl_bernoulli_independent_without_Z",
        model_type="fully_random", 
        fit_with_Z=False
    )

if which == 8:
    print("\nWith unobservables (Bernoullian outcome + independent decisions)")
    
    print("Decision-maker in the data and model: y ~ x + z and y ~ x")

    dg = lambda: bernoulliDGWithUnobservables(N_total=N_sim)

    decider = lambda x: quantileDecider(
        x, featureX_col="X", featureZ_col='Z', nJudges_M=M_sim, beta_X=1, beta_Z=1)

    perfComp(
        dg, lambda x: decider(x),
        "Fluctuation of failure rate estimates across iterations\n" +
        "Bernoulli + independent decisions, without unobservables",
        figure_path + "sl_bernoulli_independent_without_Z",
        model_type="lr", 
        fit_with_Z=False
    )
    
if which == 9:
    print("\nWith unobservables (Bernoullian outcome + independent decisions)")
    
    print("Decision-maker in the data and model: y ~ x + z and y ~ x + z")

    dg = lambda: bernoulliDGWithUnobservables(N_total=N_sim)

    decider = lambda x: quantileDecider(
        x, featureX_col="X", featureZ_col='Z', nJudges_M=M_sim, beta_X=1, beta_Z=1)

    perfComp(
        dg, lambda x: decider(x),
        "Fluctuation of failure rate estimates across iterations\n" +
        "Bernoulli + independent decisions, without unobservables",
        figure_path + "sl_bernoulli_independent_without_Z",
        model_type="lr", 
        fit_with_Z=True
    )
    
if which == 10:
    print("\nWith unobservables (Bernoullian outcome + independent decisions)")
    
    print("Decision-maker in the data and model: biased and random")

    dg = lambda: bernoulliDGWithUnobservables(N_total=N_sim)

    decider = lambda x: biasDecider(
        x, featureX_col="X", featureZ_col='Z', nJudges_M=M_sim, beta_X=1, beta_Z=1)

    perfComp(
        dg, lambda x: decider(x),
        "Fluctuation of failure rate estimates across iterations\n" +
        "Bernoulli + independent decisions, without unobservables",
        figure_path + "sl_bernoulli_independent_without_Z",
        model_type="fully_random", 
        fit_with_Z=False
    )

if which == 11:
    print("\nWith unobservables (Bernoullian outcome + independent decisions)")
    
    print("Decision-maker in the data and model: biased and y ~ x")

    dg = lambda: bernoulliDGWithUnobservables(N_total=N_sim)

    decider = lambda x: biasDecider(
        x, featureX_col="X", featureZ_col='Z', nJudges_M=M_sim, beta_X=1, beta_Z=1)

    perfComp(
        dg, lambda x: decider(x),
        "Fluctuation of failure rate estimates across iterations\n" +
        "Bernoulli + independent decisions, without unobservables",
        figure_path + "sl_bernoulli_independent_without_Z",
        model_type="lr", 
        fit_with_Z=False
    )

if which == 12:
    print("\nWith unobservables (Bernoullian outcome + independent decisions)")
    
    print("Decision-maker in the data and model: biased and y ~ x + z")

    dg = lambda: bernoulliDGWithUnobservables(N_total=N_sim)

    decider = lambda x: biasDecider(
        x, featureX_col="X", featureZ_col='Z', nJudges_M=M_sim, beta_X=1, beta_Z=1)

    perfComp(
        dg, lambda x: decider(x),
        "Fluctuation of failure rate estimates across iterations\n" +
        "Bernoulli + independent decisions, without unobservables",
        figure_path + "sl_bernoulli_independent_without_Z",
        model_type="lr", 
        fit_with_Z=True
    )
    
if which == 13:
    print("\nWith unobservables (Bernoullian outcome + independent decisions)")
    
    print("Fewer subjects per decision-maker, from 500 to 100. Now N_total =", N_sim / 5)

    dg = lambda: bernoulliDGWithUnobservables(N_total=int(N_sim / 5))

    decider = lambda x: quantileDecider(
        x, featureX_col="X", featureZ_col=None, nJudges_M=M_sim, beta_X=1, beta_Z=1)

    perfComp(
        dg, lambda x: decider(x),
        "Fluctuation of failure rate estimates across iterations\n" +
        "Bernoulli + independent decisions, without unobservables",
        figure_path + "sl_bernoulli_independent_without_Z",
        model_type="lr", 
        fit_with_Z=True
    )
    
if which == 14:
    print("\nWith unobservables (Bernoullian outcome + independent decisions)")
    
    print("Now R ~ Uniform(0.1, 0.4)")

    dg = lambda: bernoulliDGWithUnobservables(N_total=N_sim)

    decider = lambda x: quantileDecider(x, featureX_col="X", featureZ_col=None, 
                                        nJudges_M=M_sim, beta_X=1, beta_Z=1,
                                        leniency_upper_limit=0.4)

    perfComp(
        dg, lambda x: decider(x),
        "Fluctuation of failure rate estimates across iterations\n" +
        "Bernoulli + independent decisions, without unobservables",
        figure_path + "sl_bernoulli_independent_without_Z",
        model_type="lr", 
        fit_with_Z=True
    )