def weights(t):
#must accept one arguement, even though it is not used here
s = KaplanMeier(dta, 0, censoring=2)
s.fit()
s = s.results[0][0]
s = s * (1 - s)
return s
def weights(t):
#must accept one arguement, even though it is not used here
s = KaplanMeier(dta,0,censoring=2)
s.fit()
s = s.results[0][0]
s = s * (1 - s)
return s
def competing_risk(data, outcome, tt_outcome, use_kmf):
"""Gets the probability of the event for all subjects
Notes
-----
This is used for the net benefit associated with treating all patients
Parameters
----------
data : pd.DataFrame
the dataset to analyze
outcome : str
the column in `data` with outcome values
tt_outcome : str
the column in `data` with times to the outcome values
use_kmf : bool
the algorithm to use for fitting the survival curve
if `True`, use KaplanMeier; if `False`, use cumulative increase
Returns
-------
"""
raise NotImplementedError()
#construct a new dataframe of just the outcome and tt_outcome columns
df = pd.DataFrame({outcome: data[outcome].values,
tt_outcome: data[tt_outcome].values})
if use_kmf:
from statsmodels.sandbox.survival2 import KaplanMeier
kmf = KaplanMeier(df.values, 1)
kmf.fit()
else: # use cuminc
pass
import statsmodels.api as sm
import matplotlib.pyplot as plt
import numpy as np
from statsmodels.sandbox.survival2 import KaplanMeier
#Getting the strike data as an array
dta = sm.datasets.strikes.load(as_pandas=False)
print('basic data')
print('\n')
dta = list(dta.values()[-1])
print(dta[lrange(5), :])
print('\n')
#Create the KaplanMeier object and fit the model
km = KaplanMeier(dta, 0)
km.fit()
#show the results
km.plot()
print('basic model')
print('\n')
km.summary()
print('\n')
#Mutiple survival curves
km2 = KaplanMeier(dta, 0, exog=1)
km2.fit()
print('more than one curve')
import statsmodels.api as sm import matplotlib.pyplot as plt import numpy as np from statsmodels.sandbox.survival2 import KaplanMeier #Getting the strike data as an array dta = sm.datasets.strikes.load() print 'basic data' print '\n' dta = dta.values()[-1] print dta[range(5),:] print '\n' #Create the KaplanMeier object and fit the model km = KaplanMeier(dta,0) km.fit() #show the results km.plot() print 'basic model' print '\n' km.summary() print '\n' #Mutiple survival curves km2 = KaplanMeier(dta,0,exog=1) km2.fit() print 'more than one curve'