R和Python中的广义线性模型与MATLAB结果不同

3 投票
1 回答
2812 浏览
提问于 2025-04-18 14:37

我正在尝试把一个处理广义线性模型的函数从MATLAB转到R和Python。目前,R和Python给出的结果是一样的,但和MATLAB的结果不同,即使输入是相同的。

MATLAB的代码大致是这样的:

coeffs = glmfit(X, [y ones(length(y),1)], 'binomial', 'link', 'logit');

需要注意的是,MATLAB中的glmfit函数已经把常数项加到了X里面。这个函数的说明文档在这里: http://www.mathworks.com/help/stats/glmfit.html

在R中,我的代码大概是这样的:

model <- glm(cbind(y, array(1, length(y)))~X, family=binomial(link=logit))
coeffs <- model$coefficients

R中glm函数的说明文档在这里: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/glm.html

在Python中,我的代码是这样的:

import statsmodels.api as sm
import numpy as np

est = sm.GLM(sm.add_constant(y, prepend=False), sm.add_constant(X), family=sm.families.Binomial()).fit()
coeffs = est.params

Python中的说明文档在这里: http://statsmodels.sourceforge.net/devel/glm.html#module-reference

根据文档,所有的GLM函数都使用logit参数作为link。如果我猜的话,这个问题可能和MATLAB的精度有关,我之前也遇到过这个问题。(似乎MATLAB的精度没有R或NumPy高。)不过,我还是想确认一下,我是不是在使用这些函数时出了什么错。

我这样想,是不是MATLAB有问题,还是我漏掉了什么参数/做错了什么?

另外,如果重要的话,这些是我使用的变量(我从一个csv文件读取):

X =

3.00000,9.10000,0.43000,-1.26000,-0.25000,0.05000,-4.01000,7.87000
4.00000,1.70000,-1.10000,0.43000,-1.26000,-17.34000,0.05000,-4.01000
5.00000,3.80000,-0.01000,-1.10000,0.43000,5.35000,-17.34000,0.05000
6.00000,0.70000,0.13000,-0.01000,-1.10000,6.03000,5.35000,-17.34000
7.00000,5.80000,0.38000,0.13000,-0.01000,18.10000,6.03000,5.35000
8.00000,8.90000,0.13000,0.38000,0.13000,-1.88000,18.10000,6.03000
9.00000,7.60000,-0.88000,0.13000,0.38000,-7.59000,-1.88000,18.10000
10.00000,4.50000,0.01000,-0.88000,0.13000,2.67000,-7.59000,-1.88000
11.00000,2.40000,0.37000,0.01000,-0.88000,12.76000,2.67000,-7.59000
12.00000,6.60000,0.60000,0.37000,0.01000,3.25000,12.76000,2.67000
13.00000,2.80000,-1.66000,0.60000,0.37000,18.67000,3.25000,12.76000
14.00000,7.20000,1.91000,-1.66000,0.60000,-0.37000,18.67000,3.25000
15.00000,4.00000,0.78000,1.91000,-1.66000,-8.18000,-0.37000,18.67000
16.00000,7.00000,-0.81000,0.78000,1.91000,8.86000,-8.18000,-0.37000
17.00000,0.50000,0.33000,-0.81000,0.78000,-12.55000,8.86000,-8.18000
18.00000,4.00000,1.17000,0.33000,-0.81000,-10.02000,-12.55000,8.86000
19.00000,7.10000,0.20000,1.17000,0.33000,5.50000,-10.02000,-12.55000
20.00000,3.20000,0.12000,0.20000,1.17000,-20.33000,5.50000,-10.02000
3.00000,0.70000,-1.26000,-0.96000,0.88000,-12.24000,7.18000,0.31000
4.00000,1.60000,-0.70000,-1.26000,-0.96000,-1.32000,-12.24000,7.18000
5.00000,8.00000,1.98000,-0.70000,-1.26000,6.75000,-1.32000,-12.24000
6.00000,3.40000,0.80000,1.98000,-0.70000,17.24000,6.75000,-1.32000
7.00000,8.00000,-1.09000,0.80000,1.98000,-7.76000,17.24000,6.75000
8.00000,7.30000,-0.59000,-1.09000,0.80000,-2.04000,-7.76000,17.24000
9.00000,7.70000,0.69000,-0.59000,-1.09000,7.18000,-2.04000,-7.76000
10.00000,6.60000,0.09000,0.69000,-0.59000,-1.75000,7.18000,-2.04000
11.00000,0.10000,0.10000,0.09000,0.69000,1.37000,-1.75000,7.18000
12.00000,5.80000,0.18000,0.10000,0.09000,14.02000,1.37000,-1.75000
13.00000,9.90000,-1.36000,0.18000,0.10000,-3.16000,14.02000,1.37000
14.00000,1.30000,-0.26000,-1.36000,0.18000,6.47000,-3.16000,14.02000
15.00000,8.40000,-0.52000,-0.26000,-1.36000,-7.12000,6.47000,-3.16000
16.00000,7.40000,-0.66000,-0.52000,-0.26000,-1.73000,-7.12000,6.47000
17.00000,8.30000,-0.63000,-0.66000,-0.52000,11.65000,-1.73000,-7.12000
18.00000,6.70000,-1.04000,-0.63000,-0.66000,13.63000,11.65000,-1.73000
19.00000,5.80000,-1.25000,-1.04000,-0.63000,-14.71000,13.63000,11.65000
20.00000,3.20000,-1.37000,-1.25000,-1.04000,-3.60000,-14.71000,13.63000
3.00000,2.30000,-1.16000,-0.19000,-0.70000,8.41000,0.18000,-4.08000
4.00000,1.90000,1.11000,-1.16000,-0.19000,-0.00000,8.41000,0.18000
5.00000,6.40000,0.12000,1.11000,-1.16000,17.86000,-0.00000,8.41000
6.00000,3.90000,1.30000,0.12000,1.11000,1.12000,17.86000,-0.00000
7.00000,0.80000,-0.63000,1.30000,0.12000,-1.33000,1.12000,17.86000
8.00000,7.60000,-0.21000,-0.63000,1.30000,22.26000,-1.33000,1.12000
9.00000,0.90000,1.47000,-0.21000,-0.63000,-14.10000,22.26000,-1.33000
10.00000,5.10000,0.60000,1.47000,-0.21000,13.36000,-14.10000,22.26000
11.00000,6.40000,-1.41000,0.60000,1.47000,-5.37000,13.36000,-14.10000
12.00000,3.30000,-0.97000,-1.41000,0.60000,6.47000,-5.37000,13.36000
13.00000,8.80000,1.82000,-0.97000,-1.41000,8.82000,6.47000,-5.37000
14.00000,9.90000,1.80000,1.82000,-0.97000,5.63000,8.82000,6.47000
15.00000,3.40000,-0.60000,1.80000,1.82000,5.02000,5.63000,8.82000
16.00000,5.90000,-1.14000,-0.60000,1.80000,10.93000,5.02000,5.63000
17.00000,3.00000,0.77000,-1.14000,-0.60000,-2.12000,10.93000,5.02000
18.00000,1.60000,1.69000,0.77000,-1.14000,-2.89000,-2.12000,10.93000
19.00000,1.10000,-0.07000,1.69000,0.77000,-2.78000,-2.89000,-2.12000
20.00000,0.70000,-0.66000,-0.07000,1.69000,2.82000,-2.78000,-2.89000
3.00000,1.30000,-0.72000,1.12000,1.41000,4.24000,5.35000,-0.62000
4.00000,4.80000,-1.18000,-0.72000,1.12000,8.91000,4.24000,5.35000
5.00000,1.30000,-0.44000,-1.18000,-0.72000,1.15000,8.91000,4.24000
6.00000,8.90000,-0.42000,-0.44000,-1.18000,11.64000,1.15000,8.91000
7.00000,6.20000,0.06000,-0.42000,-0.44000,20.74000,11.64000,1.15000
8.00000,6.60000,1.83000,0.06000,-0.42000,21.14000,20.74000,11.64000
9.00000,2.80000,1.50000,1.83000,0.06000,-16.67000,21.14000,20.74000
10.00000,8.70000,-1.72000,1.50000,1.83000,9.86000,-16.67000,21.14000
11.00000,6.90000,-1.51000,-1.72000,1.50000,14.44000,9.86000,-16.67000
12.00000,1.80000,-1.10000,-1.51000,-1.72000,3.64000,14.44000,9.86000
13.00000,4.30000,-1.34000,-1.10000,-1.51000,13.78000,3.64000,14.44000
14.00000,4.90000,-0.31000,-1.34000,-1.10000,-2.43000,13.78000,3.64000
15.00000,2.50000,1.69000,-0.31000,-1.34000,5.85000,-2.43000,13.78000
16.00000,1.70000,0.67000,1.69000,-0.31000,-0.94000,5.85000,-2.43000
17.00000,8.00000,1.23000,0.67000,1.69000,-2.19000,-0.94000,5.85000
18.00000,1.40000,0.63000,1.23000,0.67000,14.29000,-2.19000,-0.94000
19.00000,5.00000,1.10000,0.63000,1.23000,7.65000,14.29000,-2.19000
20.00000,4.10000,-0.13000,1.10000,0.63000,3.03000,7.65000,14.29000
3.00000,3.70000,-0.79000,-1.32000,-0.62000,-5.73000,0.93000,-0.62000
4.00000,0.90000,1.56000,-0.79000,-1.32000,3.27000,-5.73000,0.93000
5.00000,6.90000,2.39000,1.56000,-0.79000,11.63000,3.27000,-5.73000
6.00000,0.20000,1.17000,2.39000,1.56000,2.61000,11.63000,3.27000
7.00000,2.30000,-0.83000,1.17000,2.39000,18.57000,2.61000,11.63000
8.00000,0.10000,0.08000,-0.83000,1.17000,2.48000,18.57000,2.61000
9.00000,0.80000,-0.52000,0.08000,-0.83000,-2.11000,2.48000,18.57000
10.00000,3.40000,0.47000,-0.52000,0.08000,-5.58000,-2.11000,2.48000
11.00000,3.30000,-1.37000,0.47000,-0.52000,-5.09000,-5.58000,-2.11000
12.00000,7.70000,-0.54000,-1.37000,0.47000,-13.45000,-5.09000,-5.58000
13.00000,7.80000,2.62000,-0.54000,-1.37000,13.67000,-13.45000,-5.09000
14.00000,3.50000,0.91000,2.62000,-0.54000,-10.03000,13.67000,-13.45000
15.00000,4.40000,-0.73000,0.91000,2.62000,7.27000,-10.03000,13.67000
16.00000,0.50000,0.53000,-0.73000,0.91000,-5.44000,7.27000,-10.03000
17.00000,4.50000,-0.80000,0.53000,-0.73000,7.60000,-5.44000,7.27000
18.00000,7.90000,0.01000,-0.80000,0.53000,-11.30000,7.60000,-5.44000
19.00000,6.30000,0.42000,0.01000,-0.80000,9.38000,-11.30000,7.60000
20.00000,2.70000,0.13000,0.42000,0.01000,3.55000,9.38000,-11.30000

y =

0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
1.00000
1.00000
0.00000
1.00000
1.00000
0.00000
0.00000
0.00000
1.00000
0.00000
1.00000
0.00000
0.00000
1.00000
1.00000
0.00000
1.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
1.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
1.00000
1.00000
0.00000
0.00000
1.00000
0.00000
1.00000
1.00000
0.00000
0.00000
0.00000
1.00000
1.00000
0.00000
1.00000
0.00000
1.00000
0.00000
0.00000
0.00000
0.00000
1.00000
0.00000
0.00000
1.00000
1.00000
0.00000
1.00000
1.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
1.00000
0.00000
0.00000
0.00000

谢谢大家的帮助!

1 个回答

1

感谢aosmith,我找到了问题的解决办法 --

我的R代码改成了:

y <- cbind(y, 1-y)
model <- glm(y~X, family=binomial(link=logit))
coeffs <- model$coeffs

而我的Python代码改成了:

est = sm.GLM(np.vstack([y, 1-y]).T, sm.add_constant(X), family=sm.families.Binomial()).fit()
coeffs = est.params

谢谢你!

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