使用python和rpy2绘制拟合模型的预测值

Question

我卡住了使用rpy2绘制观察值与预测值。下面的R代码可以工作:

                                                 #==============================#
                                                 # Set up the data.             #
                                                 #==============================#
yTmp <- seq(1,20)
y <- NULL
x1Tmp <- seq(1,20)
x1 <- NULL
x2Tmp <- seq(1,20)
x2 <- NULL
for (i in 1:20)
{
    y[i] = yTmp[i] + runif(1, 0, 1)
    x1[i] = x1Tmp[i] + runif(1, 0, 1)
    x2[i] = x2Tmp[i] + runif(1, 0, 1)
}
                                                #==============================#
                                                # Fit the model.               #
                                                #==============================#
fittedModel <- lm(y ~ x1 + x2)
                                                #==============================#
                                                # Plot the observed vs predicted
                                                #==============================#
png(filename="fittedModel_R.png")
plot(fittedModel$fitted.values, y)
dev.off()

生成一个很好的观察和预测的情节(我会发布，但我需要至少10个声誉来发布图像)。

我试图使用rpy2复制这个，但我无法弄清楚如何让拟合值很好地发挥作用。下面的代码与上面的R代码一样，但是不起作用:

#!/usr/bin/env python
                                                #==============================#
                                                # Set up packages.             #
                                                #==============================#
import rpy2.robjects as robjects
from rpy2.robjects.packages import importr
import random
import string
stats = importr('stats')
from rpy2.robjects import Formula
lattice = importr('lattice')
rprint = robjects.globalenv.get("print")
grdevices = importr('grDevices')
                                                #==============================#
                                                # Set up the data.             #
                                                #==============================#
y = robjects.FloatVector(())
x1 = robjects.FloatVector(())
x2 = robjects.FloatVector(())
for i in range(1,20):
    yValue = i + random.random()
    y.rx[i] = yValue
    x1Value = i + random.random()
    x1.rx[i] = x1Value
    x2Value = i + random.random()
    x2.rx[i] = x2Value
robjects.globalenv["y"] = y
robjects.globalenv["x1"] = x1
robjects.globalenv["x2"] = x2
                                                #==============================#
                                                # Fit the model.               #
                                                #==============================#
fittedModel = stats.lm("y ~ x1 + x2")
                                                #==============================#
                                                # Attempt to extract the fitted 
                                                # values from the model and put
                                                # on a vector.                 #
                                                #==============================#
robjects.globalenv['predicted'] = robjects.Vector(fittedModel.rx('fitted.values'))
                                                #==============================#
                                                # Plot the observed vs predicted
                                                #==============================#
grdevices.png(file = "fittedModel_RPY2.png", width = 512, height = 512)
formula = Formula('y ~ predicted')
p = lattice.xyplot(formula)
rprint(p)
grdevices.dev_off()

生成一个错误:

/usr/地方/lib/python2.7/dist-packages/rpy2 robjects/functions.py: 106:UserWarning: Error in order(as.numeric(x)): (list)对象不能强制输入'double'

res = super(Function, self)。调用 (* new_args, * * new_kwargs)回溯(最近一次调用):文件"./testRPY2.py"，第45行，inp = lattice.xyplot(公式)文件"/usr/local/lib/python2.7/dist-packages/rpy2/objects/functions.py"，第178行，在中调用返回super(SignatureTranslatedFunction, self)。call(*args， **kwargs) File "/usr/local/lib/python2.7/dist-packages/rpy2/objects/functions.py"，第106行，在中调用res = super(Function, self)。call(*new_args， **new_kwargs) rpy2.rinterface。runtimeerror: Error in order(as.numeric(x)):
(list)对象不能强制类型为'double'

问题肯定是与预测值有关，因为改变公式来绘制y vs y会生成一个漂亮的图:

formula = Formula('y ~ y')

我曾多次尝试将python中的数据强制转换为可绘图格式，包括在python中转换为字符串，操作并将其作为浮动向量发送回rpy2。但我真的不明白为什么它不起作用，一定有更好的方法。如果您对我的问题有任何见解和帮助，我将不胜感激。

Answer 1

如果对predicted有任何疑问，您可以尝试

print(type(robjects.globalenv['predicted']))

或更长的输出:

print(robjects.globalenv['predicted'])

你会发现你在R中没有所谓的原子向量，这几乎可以肯定来自创建predicted的那一行:

robjects.globalenv['predicted'] = robjects.Vector(fittedModel.rx('fitted.values'))

方法.rx()对应R的[, .rx2()对应R的[[。您将需要后者。

文档中的介绍有一个简短的R的lm()示例:http://rpy.sourceforge.net/rpy2/doc - 2.6 -/- html/introduction.html #线性模型