我正在尝试对德国信用数据运行logit回归(www4.stat.ncsu.edu/~boos/var.select/german.credit.html).为了测试代码,我只使用了数值变量,并尝试使用以下代码对结果进行回归.
import pandas as pd
import statsmodels.api as sm
import pylab as pl
import numpy as np
df = pd.read_csv("germandata.txt",delimiter=' ')
df.columns = ["chk_acc","duration","history","purpose","amount","savings_acc","employ_since","install_rate","pers_status","debtors","residence_since","property","age","other_plans","housing","existing_credit","job","no_people_liab","telephone","foreign_worker","admit"]
#pls note that I am only retaining numeric variables
cols_to_keep = ['admit','duration', 'amount', 'install_rate','residence_since','age','existing_credit','no_people_liab']
# rank of cols_to_keep is 8
print np.linalg.matrix_rank(df[cols_to_keep].values)
data = df[cols_to_keep]
data['intercept'] = 1.0
train_cols = data.columns[1:]
#to check the rank of train_cols, which in this case is 8
print np.linalg.matrix_rank(data[train_cols].values)
#fit logit model
logit = sm.Logit(data['admit'], data[train_cols])
result = logit.fit()
检查数据时,所有8.0列都是独立的.尽管如此,我得到奇异矩阵错误.你能帮忙吗？ …