Question

我正在尝试使用rpy2让我在python中使用一些r功能。这是我想做的简单回归。我创建一个数据帧，将其转换为R数据帧，然后尝试使用R＆lt; lm。但无法找到R数据框（见下文）。我应该在哪里进行故障排除？

仅供参考我使用的是python 2.7.3，rpy2-2.3.2，pandas version＆＃39; 0.10.1＆＃39;和R2.15.3

>>> import rpy2
>>> import pandas as pd
>>> import pandas.rpy.common as com
>>> datframe = pd.DataFrame({'a' : [1, 2, 3], 'b' : [3, 4, 5]})
>>> r_df = com.convert_to_r_dataframe(datframe)
>>> r_df     
(DataFrame - Python:0x32547e8 / R:0x345d640)
[IntVector, IntVector]
  a: (class 'rpy2.robjects.vectors.IntVector')
  (IntVector - Python:0x3254e18 / R:0x345d608)
[       1,        2,        3]
  b: (class 'rpy2.robjects.vectors.IntVector')
  (IntVector - Python:0x3254e60 / R:0x345d5d0)
[       3,        4,        5]
>>> print type(r_df)
(class 'rpy2.robjects.vectors.DataFrame')
>>> from rpy2.robjects import r
>>> r('lmout <- lm(r_df$a ~ r_df$b)')

Error in eval(expr, envir, enclos) : object 'r_df' not found
Traceback (most recent call last):
  File "<pyshell#8>", line 1, in <module>
    r('lmout <- lm(r_df$a ~ r_df$b)')
  File "/usr/local/lib/python2.7/dist-packages/rpy2/robjects/__init__.py", line 236, in __call__
    res = self.eval(p)
  File "/usr/local/lib/python2.7/dist-packages/rpy2/robjects/functions.py", line 86, in __call__
    return super(SignatureTranslatedFunction, self).__call__(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/rpy2/robjects/functions.py", line 35, in __call__
    res = super(Function, self).__call__(*new_args, **new_kwargs)
RRuntimeError: Error in eval(expr, envir, enclos) : object 'r_df' not found

Answer 1

致电

r('lmout <- lm(r_df$a ~ r_df$b)')

嵌入式R将查找变量r_df，并且代码示例中的R对此变量不可见。

做的时候

r_df = com.convert_to_r_dataframe(datframe)

你在Python端创建变量r_df但是现在在R中的实际数据中没有与R相关的符号（名称）。这个数据结构仍然是匿名的。（顺便说一句，您可能希望使用rpy2-2.3.3附带的pandas数据帧的自动转换）。

要在R的“全局环境”中创建变量名称，请添加：

from rpy2.robjects import globalenv
globalenv['r_df'] = r_df

现在您的lm()电话应该有效。

Answer 2

试试这个，（不确定哪个标题可以做到魔术，但是......）

import rpy2.robjects as robjects
from rpy2.robjects import DataFrame, Formula
import rpy2.robjects.numpy2ri as npr
import numpy as np
from rpy2.robjects.packages import importr


def my_linear_fit_using_r(X,Y,verbose=True):
   # ## FITTINGS:   RPy implementation ###
   r_correlation = robjects.r('function(x,y) cor.test(x,y)')
   # r_quadfit = robjects.r('function(x,y) lm(y~I(x)+I(x^2))')
   r_linfit = robjects.r('function(x,y) lm(y~x)')
   r_get_r2=robjects.r('function(x) summary(x)$r.squared')
   lin=r_linfit(robjects.FloatVector(X),robjects.FloatVector(Y))
   coef_lin=robjects.r.coef(lin)
   a=coef_lin[0]
   b=coef_lin[1]
   r2=r_get_r2(lin)
   ci=robjects.r.confint(lin) # confidence intervals
   lwr_a=ci[0]
   lwr_b=ci[1]
   upr_a=ci[2]
   upr_b=ci[3]
   if verbose:
      print robjects.r.summary(lin)
      # print robjects.r.summary(quad)
   return (a,b,r2[0],lwr_a,upr_a,lwr_b,upr_b)

Answer 3

请注意，对于简单的回归，您可以在Python中完全执行此操作，使用ols中的statsmodels：

from statsmodels.formula.api import ols

lmout = ols('a ~ b', datframe).fit()
lmout.summary()

找不到rpy2对象错误

3 个答案: