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
import scikits.statsmodels.api as sm import matplotlib.pyplot as plt import numpy as np from scikits.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'
import scikits.statsmodels.api as sm import matplotlib.pyplot as plt import numpy as np from scikits.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'