置信区间外的子集的数据点(Subset data points outside confidence

使用相同的例子，从这个以前的问题（粘贴下面的代码），我们可以用得到95％CI summary_table从功能statsmodels outliers_influence 。但现在，怎么会是可能只子集的数据点（ x和y是置信区间之外）？

import numpy as np
import statsmodels.api as sm
from statsmodels.stats.outliers_influence import summary_table

#measurements genre
n = 100
x = np.linspace(0, 10, n)
e = np.random.normal(size=n)
y = 1 + 0.5*x + 2*e
X = sm.add_constant(x)
re = sm.OLS(y, X).fit()
st, data, ss2 = summary_table(re, alpha=0.05)
predict_ci_low, predict_ci_upp = data[:, 6:8].T

这可能是这个有点晚了，但你可以把它放在一个pandas.DataFrame和过滤器根据布尔的名单上。假设我得到了你的问题：

import numpy as np
import statsmodels.api as sm
from statsmodels.stats.outliers_influence import summary_table
import matplotlib.pyplot as plot

## Import pandas
import pandas as pd

#measurements genre
n = 100
x = np.linspace(0, 10, n)
e = np.random.normal(size=n)
y = 1 + 0.5*x + 2*e
X = sm.add_constant(x)
re = sm.OLS(y, X).fit()
st, data, ss2 = summary_table(re, alpha=0.05)

# Make prediction
prediction = re.predict(X)
predict_ci_low, predict_ci_upp = data[:, 6:8].T

# Put y and x in a pd.DataFrame
df = pd.DataFrame(y).set_index(x)

# Get the y values that are out of the ci intervals. This could be done directly in the df indexer
out_up = y > predict_ci_upp
out_down = y < predict_ci_low

# Plot everything
plot.plot(x, y, label = 'train')
plot.plot(df[out_up], marker = 'o', linewidth = 0)
plot.plot(df[out_down], marker = 'o', linewidth = 0)
plot.plot(x, predictionTrain, label = 'prediction')
plot.plot(x, predict_ci_upp, label = 'ci_up')
plot.plot(x, predict_ci_low, label = 'ci_low')
plot.legend(loc='best')

下面是导致情节：