我很困惑为什么 R 和 statsmodels 中的逻辑回归模型不一致。
如果我在 R 中准备一些数据
# From https://courses.edx.org/c4x/MITx/15.071x/asset/census.csv
library(caTools) # for sample.split
census = read.csv("census.csv")
set.seed(2000)
split = sample.split(census$over50k, SplitRatio = 0.6)
censusTrain = subset(census, split==TRUE)
censusTest = subset(census, split==FALSE)
然后运行逻辑回归
CensusLog1 = glm(over50k ~., data=censusTrain, family=binomial)
I see results https://gist.github.com/orome/9840196 like
Estimate Std. Error z value Pr(>|z|)
(Intercept) -8.658e+00 1.379e+00 -6.279 3.41e-10 ***
age 2.548e-02 2.139e-03 11.916 < 2e-16 ***
workclass Federal-gov 1.105e+00 2.014e-01 5.489 4.03e-08 ***
workclass Local-gov 3.675e-01 1.821e-01 2.018 0.043641 *
workclass Never-worked -1.283e+01 8.453e+02 -0.015 0.987885
workclass Private 6.012e-01 1.626e-01 3.698 0.000218 ***
workclass Self-emp-inc 7.575e-01 1.950e-01 3.884 0.000103 ***
workclass Self-emp-not-inc 1.855e-01 1.774e-01 1.046 0.295646
workclass State-gov 4.012e-01 1.961e-01 2.046 0.040728 *
workclass Without-pay -1.395e+01 6.597e+02 -0.021 0.983134
...
但我在 Python 中使用相同的数据，首先使用 R 导出
write.csv(censusTrain,file="traincensus.csv")
write.csv(censusTest,file="testcensus.csv")
然后导入到Python中
import pandas as pd
census = pd.read_csv("census.csv")
census_train = pd.read_csv("traincensus.csv")
census_test = pd.read_csv("testcensus.csv")
我得到的错误和奇怪的结果与我在 R 中得到的结果没有任何关系。
如果我只是尝试
import statsmodels.api as sm
census_log_1 = sm.Logit.from_formula(f, census_train).fit()
我收到错误：
ValueError: operands could not be broadcast together with shapes (19187,2) (19187,)
即使准备数据patsy using
import patsy
f = 'over50k ~ ' + ' + '.join(list(census.columns)[:-1])
y, X = patsy.dmatrices(f, census_train, return_type='dataframe')
trying
census_log_1 = sm.Logit(y, X).fit()
导致同样的错误。我可以避免错误的唯一方法是使用 useGLM
census_log_1 = sm.GLM(y, X, family=sm.families.Binomial()).fit()
但这会产生results https://gist.github.com/orome/9839624与（我认为的）等效 R API 生成的完全不同：
coef std err t P>|t| [95.0% Conf. Int.]
----------------------------------------------------------------------------------------------------------------
Intercept 10.6766 5.985 1.784 0.074 -1.055 22.408
age -0.0255 0.002 -11.916 0.000 -0.030 -0.021
workclass[T. Federal-gov] -0.9775 4.498 -0.217 0.828 -9.794 7.839
workclass[T. Local-gov] -0.2395 4.498 -0.053 0.958 -9.055 8.576
workclass[T. Never-worked] 8.8346 114.394 0.077 0.938 -215.374 233.043
workclass[T. Private] -0.4732 4.497 -0.105 0.916 -9.288 8.341
workclass[T. Self-emp-inc] -0.6296 4.498 -0.140 0.889 -9.446 8.187
workclass[T. Self-emp-not-inc] -0.0576 4.498 -0.013 0.990 -8.873 8.758
workclass[T. State-gov] -0.2733 4.498 -0.061 0.952 -9.090 8.544
workclass[T. Without-pay] 10.0745 85.048 0.118 0.906 -156.616 176.765
...
为什么 Python 中的逻辑回归会产生错误并且结果与 R 产生的结果不同？这些 API 实际上不是等效的吗（我之前已经让它们工作过以产生相同的结果）？是否需要对数据集进行一些额外的处理才能使其可供 statsmodels 使用？
