I am running a regression as follows (df
is a pandas
dataframe):
import statsmodels.api as sm
est = sm.OLS(df['p'], df[['e', 'varA', 'meanM', 'varM', 'covAM']]).fit()
est.summary()
Which gave me, among others, an R-squared of 0.942
. So then I wanted to plot the original y-values
and the fitted values. For this, I sorted the original values:
orig = df['p'].values
fitted = est.fittedvalues.values
args = np.argsort(orig)
import matplotlib.pyplot as plt
plt.plot(orig[args], 'bo')
plt.plot(orig[args]-resid[args], 'ro')
plt.show()
This, however, gave me a graph where the values were completely off. Nothing that would suggest an R-squared of 0.9
. Therefore, I tried to calculate it manually myself:
yBar = df['p'].mean()
SSTot = df['p'].apply(lambda x: (x-yBar)**2).sum()
SSReg = ((est.fittedvalues - yBar)**2).sum()
1 - SSReg/SSTot
Out[79]: 0.2618159806908984
Am I doing something wrong? Or is there a reason why my computation is so far off what statsmodels is getting? SSTot
, SSReg
have values of 48084
, 35495
.
See Question&Answers more detail:
os 与恶龙缠斗过久,自身亦成为恶龙;凝视深渊过久,深渊将回以凝视…