我有一些看起来像这样的OLS结果：
OLS Regression Results
==============================================================================
Dep. Variable: dependent_variable R-squared: 0.364
Model: OLS Adj. R-squared: 0.328
Method: Least Squares F-statistic: 10.09
Date: Fri, 09 Mar 2018 Prob (F-statistic): 4.74e-20
Time: 12:11:10 Log-Likelihood: 210.15
No. Observations: 299 AIC: -386.3
Df Residuals: 282 BIC: -323.4
Df Model: 16
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const -1.4285 0.543 -2.629 0.009 -2.498 -0.359
SIM 0.0481 0.021 2.290 0.023 0.007 0.090
OIL 0.0902 0.080 1.124 0.262 -0.068 0.248
DEF -0.0890 0.098 -0.907 0.365 -0.282 0.104
PE -2.8715 0.638 -4.500 0.000 -4.128 -1.615
VRP -0.6240 0.079 -7.866 0.000 -0.780 -0.468
CAPE -2.5268 0.502 -5.035 0.000 -3.515 -1.539
BM 1.0856 0.291 3.730 0.000 0.513 1.659
CAY -0.1232 0.036 -3.411 0.001 -0.194 -0.052
BDI 0.0533 0.070 0.766 0.444 -0.084 0.190
DP 1.5659 0.316 4.957 0.000 0.944 2.188
MA -0.0518 0.077 -0.675 0.500 -0.203 0.099
NOS -0.0847 0.048 -1.778 0.076 -0.179 0.009
PCAval 5.4010 1.117 4.834 0.000 3.202 7.600
CPI -0.0837 0.075 -1.118 0.264 -0.231 0.064
BY -0.1682 0.061 -2.779 0.006 -0.287 -0.049
TERM 0.0871 0.045 1.951 0.052 -0.001 0.175
==============================================================================
Omnibus: 4.654 Durbin-Watson: 1.904
Prob(Omnibus): 0.098 Jarque-Bera (JB): 4.401
Skew: 0.289 Prob(JB): 0.111
Kurtosis: 3.134 Cond. No. 458.
==============================================================================
我想为这个回归生成一个很好的情节，但是我不确定是否有任何用于绘制多变量回归的库。到目前为止，没有任何工作，我已经尝试了here列出的答案，但没有用。
相关DF看起来像：
SIM OIL DEF PE VRP ERP4M CAPE \
SIM 1.000000 -0.050460 0.083599 -0.034125 -0.150744 0.064383 0.020047
OIL -0.050460 1.000000 -0.422509 0.037500 -0.043082 0.526773 0.248337
DEF 0.083599 -0.422509 1.000000 0.621826 0.200023 -0.574159 -0.787776
PE -0.034125 0.037500 0.621826 1.000000 0.151396 -0.004591 -0.624840
VRP -0.150744 -0.043082 0.200023 0.151396 1.000000 -0.146723 -0.374840
ERP4M 0.064383 0.526773 -0.574159 -0.004591 -0.146723 1.000000 0.272633
CAPE 0.020047 0.248337 -0.787776 -0.624840 -0.374840 0.272633 1.000000
BM -0.013919 -0.278438 0.667338 0.425422 0.470390 -0.244372 -0.940337
CAY -0.012688 -0.042881 0.423774 0.155665 0.189580 -0.387483 -0.422951
BDI -0.059310 0.184701 0.014964 0.175442 -0.096856 0.009685 -0.051522
DP 0.031967 -0.359831 0.910237 0.749141 0.250744 -0.445332 -0.867026
MA -0.011023 0.160401 -0.816400 -0.751237 -0.051251 0.398050 0.598858
NOS 0.057399 -0.292519 0.501947 0.233667 -0.026792 -0.406684 -0.297782
PCAval -0.046081 0.433105 -0.294880 0.384547 -0.278818 0.422584 0.463201
SPX 0.037689 0.366747 -0.245153 0.110772 -0.489112 0.551562 0.103475
CPI -0.002426 0.302854 -0.401922 -0.613360 -0.106276 -0.149682 0.539511
BY 0.028855 0.320562 -0.383879 0.002436 -0.086611 0.419317 0.292955
TERM 0.025035 -0.025416 0.255060 0.316007 0.346241 0.082039 -0.623297
ERP1M 0.037997 0.361399 -0.235481 0.117769 -0.477621 0.553413 0.079395
我想打印每个变量与ERP4M的相关性。
corr = df.corr(method='pearson').loc[['ERP4M']]
smg.plot_corr(corr_matrix, xnames=list(corr))
plt.show()