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)

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,首先从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()

我收到一个错误：

ValueError: operands could not be broadcast together with shapes (19187,2) (19187,)

即使用patsy准备数据也是如此

import patsy

f = 'over50k ~ ' + ' + '.join(list(census.columns)[:-1])

y,X = patsy.dmatrices(f,census_train,return_type='dataframe')

试

census_log_1 = sm.Logit(y,X).fit()

导致相同的错误.我可以避免错误的唯一方法是使用GLM

census_log_1 = sm.GLM(y,X,family=sm.families.Binomial()).fit()

但是这会产生results,与完全不同于(我认为是)等效的R API产生的那些：

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,并且会产生与R生成的错误不同的结果？这些API实际上是不相同的(我之前已经让它们工作以产生相同的结果)？是否需要对数据集进行一些额外的处理才能使它们被statsmodel使用？