生存分析/Weibull Distribution韦布尔分布

最新推荐文章于 2025-05-21 12:50:01 发布

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最新推荐文章于 2025-05-21 12:50:01 发布

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文章标签： python matlab r语言

本文介绍了生存分析中的韦布尔分布，并提供了在保险、医疗和信用卡风控领域的应用实例。通过链接分享了相关视频教程和资料，帮助读者深入理解和运用韦布尔分布进行数据分析。

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测试脚本

测试数据

T is an array of durations, E is a either boolean or binary array representing whether the “death” was observed (alternatively an individual can be censored).

import lifelines
from lifelines.datasets import load_waltons

df = load_waltons() # returns a Pandas DataFrame

T = df['T']
E = df['E']

from lifelines import KaplanMeierFitter
kmf = KaplanMeierFitter()
kmf.fit(T, event_observed=E) # more succiently, kmf.fit(T,E)

kmf.survival_function_
'''
Out[7]: 
          KM_estimate
timeline             
0.0          1.000000
6.0          0.993865
7.0          0.987730
9.0          0.969210
13.0         0.950690
15.0         0.938344
17.0         0.932170
19.0         0.913650
22.0         0.888957
26.0         0.858090
29.0         0.827224
32.0         0.821051
33.0         0.802531
36.0         0.790184
38.0         0.777837
41.0         0.734624
43.0         0.728451
45.0         0.672891
47.0         0.666661
48.0         0.616817
51.0         0.598125

'''

kmf.median_
'''
Out[8]: 56.0
'''
kmf.plot()

import lifelines
from lifelines.datasets import load_waltons
from lifelines import KaplanMeierFitter
df = load_waltons() # returns a Pandas DataFrame

kmf = KaplanMeierFitter()
T = df['T']
E = df['E']
groups = df['group']
ix = (groups == 'miR-137')

kmf.fit(T[~ix], E[~ix], label='control')
ax = kmf.plot()

kmf.fit(T[ix], E[ix], label='miR-137')
kmf.plot(ax=ax)

import numpy as np
import matplotlib.pyplot as plt 
from lifelines.plotting import plot_lifetimes
from numpy.random import uniform, exponential

N = 25
current_time = 10
actual_lifetimes = np.array([[exponential(12), exponential(2)][uniform()<0.5] for i in range(N)])
observed_lifetimes = np.minimum(actual_lifetimes,current_time)
observed= actual_lifetimes < current_time

plt.xlim(0,25)
plt.vlines(10,0,30,lw=2, linestyles="--")
plt.xlabel('time')
plt.title('Births and deaths of our population, at $t=10$')
plot_lifetimes(observed_lifetimes, event_observed=observed)
print "Observed lifetimes at time %d:\n"%(current_time), observed_lifetimes

import pandas as pd
import lifelines
from lifelines import KaplanMeierFitter
import matplotlib.pyplot as plt

data = lifelines.datasets.load_dd()
kmf = KaplanMeierFitter()

T = data["duration"]
C = data["observed"]

kmf.fit(T, event_observed=C )
plt.title('Survival function of political regimes')
kmf.survival_function_.plot()
kmf.plot()

kmf.median_

import pandas as pd
import lifelines
from lifelines import KaplanMeierFitter
import matplotlib.pyplot as plt

data = lifelines.datasets.load_dd()
kmf = KaplanMeierFitter()

T = data["duration"]
C = data["observed"]

kmf.fit(T, event_observed=C )
plt.title('Survival function of political regimes')
kmf.survival_function_.plot()
kmf.plot()



ax = plt.subplot(111)

dem = (data["democracy"] == "Democracy")
kmf.fit(T[dem], ev