import pandas as pd
import statsmodels.api as sms
import statsmodels.formula.api as smf

if __name__ == "__main__":
    df = pd.read_csv("data/merged_data.csv")
    md_1NN = smf.mixedlm("ll_diff_1NN ~ num_papers + median_collaborators",df, groups=df["field"])
    md_proto = smf.mixedlm("ll_diff_proto ~ num_papers + median_collaborators", df, groups=df["field"])
    mdf_1NN = md_1NN.fit()
    mdf_proto = md_proto.fit()
    print("1NN")
    print(mdf_1NN.summary())
    print(mdf_1NN.aic)
    print(mdf_1NN.bic)
    print("Prototype")
    print(mdf_proto.summary())
    print(mdf_proto.aic)
    print(mdf_proto.bic)
804e11a2346dc5df7861414a95c7946c6f80a163
<mask token>

<mask token>
if __name__ == '__main__':
    df = pd.read_csv('data/merged_data.csv')
    md_1NN = smf.mixedlm('ll_diff_1NN ~ num_papers + median_collaborators',
        df, groups=df['field'])
    md_proto = smf.mixedlm('ll_diff_proto ~ num_papers + median_collaborators',
        df, groups=df['field'])
    mdf_1NN = md_1NN.fit()
    mdf_proto = md_proto.fit()
    print('1NN')
    print(mdf_1NN.summary())
    print(mdf_1NN.aic)
    print(mdf_1NN.bic)
    print('Prototype')
    print(mdf_proto.summary())
    print(mdf_proto.aic)
    print(mdf_proto.bic)

import pandas as pd
import statsmodels.api as sms
import statsmodels.formula.api as smf
if __name__ == '__main__':
    df = pd.read_csv('data/merged_data.csv')
    md_1NN = smf.mixedlm('ll_diff_1NN ~ num_papers + median_collaborators',
        df, groups=df['field'])
    md_proto = smf.mixedlm('ll_diff_proto ~ num_papers + median_collaborators',
        df, groups=df['field'])
    mdf_1NN = md_1NN.fit()
    mdf_proto = md_proto.fit()
    print('1NN')
    print(mdf_1NN.summary())
    print(mdf_1NN.aic)
    print(mdf_1NN.bic)
    print('Prototype')
    print(mdf_proto.summary())
    print(mdf_proto.aic)
    print(mdf_proto.bic)

import pandas as pd
import statsmodels.api as sms
import statsmodels.formula.api as smf

if __name__ == "__main__":
    df = pd.read_csv("data/merged_data.csv")
    md_1NN = smf.mixedlm("ll_diff_1NN ~ num_papers + median_collaborators",df, groups=df["field"])
    md_proto = smf.mixedlm("ll_diff_proto ~ num_papers + median_collaborators", df, groups=df["field"])
    mdf_1NN = md_1NN.fit()
    mdf_proto = md_proto.fit()
    print("1NN")
    print(mdf_1NN.summary())
    print(mdf_1NN.aic)
    print(mdf_1NN.bic)
    print("Prototype")
    print(mdf_proto.summary())
    print(mdf_proto.aic)
    print(mdf_proto.bic)