本文整理汇总了Python中statsmodels.duration.hazard_regression.PHReg.predict方法的典型用法代码示例。如果您正苦于以下问题:Python PHReg.predict方法的具体用法?Python PHReg.predict怎么用?Python PHReg.predict使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类statsmodels.duration.hazard_regression.PHReg
的用法示例。
在下文中一共展示了PHReg.predict方法的2个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _kernel_cumincidence
# 需要导入模块: from statsmodels.duration.hazard_regression import PHReg [as 别名]
# 或者: from statsmodels.duration.hazard_regression.PHReg import predict [as 别名]
def _kernel_cumincidence(time, status, exog, kfunc, freq_weights,
dimred=True):
"""
Calculates cumulative incidence functions using kernels.
Parameters
----------
time : array-like
The observed time values
status : array-like
The status values. status == 0 indicates censoring,
status == 1, 2, ... are the events.
exog : array-like
Covariates such that censoring becomes independent of
outcome times conditioned on the covariate values.
kfunc : function
A kernel function
freq_weights : array-like
Optional frequency weights
dimred : boolean
If True, proportional hazards regression models are used to
reduce exog to two columns by predicting overall events and
censoring in two separate models. If False, exog is used
directly for calculating kernel weights without dimension
reduction.
"""
# Reorder so time is ascending
ii = np.argsort(time)
time = time[ii]
status = status[ii]
exog = exog[ii, :]
nobs = len(time)
# Convert the unique times to ranks (0, 1, 2, ...)
utime, rtime = np.unique(time, return_inverse=True)
# Last index where each unique time occurs.
ie = np.searchsorted(time, utime, side='right') - 1
ngrp = int(status.max())
# All-cause status
statusa = (status >= 1).astype(np.float64)
if freq_weights is not None:
freq_weights = freq_weights / freq_weights.sum()
ip = []
sp = [None] * nobs
n_risk = [None] * nobs
kd = [None] * nobs
for k in range(ngrp):
status0 = (status == k + 1).astype(np.float64)
# Dimension reduction step
if dimred:
sfe = PHReg(time, exog, status0).fit()
fitval_e = sfe.predict().predicted_values
sfc = PHReg(time, exog, 1 - status0).fit()
fitval_c = sfc.predict().predicted_values
exog2d = np.hstack((fitval_e[:, None], fitval_c[:, None]))
exog2d -= exog2d.mean(0)
exog2d /= exog2d.std(0)
else:
exog2d = exog
ip0 = 0
for i in range(nobs):
if k == 0:
kd1 = exog2d - exog2d[i, :]
kd1 = kfunc(kd1)
kd[i] = kd1
# Get the local all-causes survival function
if k == 0:
denom = np.cumsum(kd[i][::-1])[::-1]
num = kd[i] * statusa
rat = num / denom
tr = 1e-15
ii = np.flatnonzero((denom < tr) & (num < tr))
rat[ii] = 0
ratc = 1 - rat
ratc = np.clip(ratc, 1e-10, np.inf)
lrat = np.log(ratc)
prat = np.cumsum(lrat)[ie]
sf = np.exp(prat)
sp[i] = np.r_[1, sf[:-1]]
n_risk[i] = denom[ie]
# Number of cause-specific deaths at each unique time.
d0 = np.bincount(rtime, weights=status0*kd[i],
minlength=len(utime))
# The cumulative incidence function probabilities. Carry
# forward once the effective sample size drops below 1.
ip1 = np.cumsum(sp[i] * d0 / n_risk[i])
jj = len(ip1) - np.searchsorted(n_risk[i][::-1], 1)
if jj < len(ip1):
#.........这里部分代码省略.........
示例2: _kernel_survfunc
# 需要导入模块: from statsmodels.duration.hazard_regression import PHReg [as 别名]
# 或者: from statsmodels.duration.hazard_regression.PHReg import predict [as 别名]
def _kernel_survfunc(time, status, exog, kfunc, freq_weights):
"""
Estimate the marginal survival function under dependent censoring.
Parameters
----------
time : array-like
The observed times for each subject
status : array-like
The status for each subject (1 indicates event, 0 indicates
censoring)
exog : array-like
Covariates such that censoring is independent conditional on
exog
kfunc : function
Kernel function
freq_weights : array-like
Optional frequency weights
Returns
-------
probs : array-like
The estimated survival probabilities
times : array-like
The times at which the survival probabilities are estimated
References
----------
Zeng, Donglin 2004. Estimating Marginal Survival Function by
Adjusting for Dependent Censoring Using Many Covariates. The
Annals of Statistics 32 (4): 1533 55.
doi:10.1214/009053604000000508.
http://arxiv.org/pdf/math/0409180.pdf
"""
# Dimension reduction step
sfe = PHReg(time, exog, status).fit()
fitval_e = sfe.predict().predicted_values
sfc = PHReg(time, exog, 1 - status).fit()
fitval_c = sfc.predict().predicted_values
exog2d = np.hstack((fitval_e[:, None], fitval_c[:, None]))
n = len(time)
ixd = np.flatnonzero(status == 1)
# For consistency with standard KM, only compute the survival
# function at the times of observed events.
utime = np.unique(time[ixd])
# Reorder everything so time is ascending
ii = np.argsort(time)
time = time[ii]
status = status[ii]
exog2d = exog2d[ii, :]
# Last index where each evaluation time occurs.
ie = np.searchsorted(time, utime, side='right') - 1
if freq_weights is not None:
freq_weights = freq_weights / freq_weights.sum()
sprob = 0.
for i in range(n):
kd = exog2d - exog2d[i, :]
kd = kfunc(kd)
denom = np.cumsum(kd[::-1])[::-1]
num = kd * status
rat = num / denom
tr = 1e-15
ii = np.flatnonzero((denom < tr) & (num < tr))
rat[ii] = 0
ratc = 1 - rat
ratc = np.clip(ratc, 1e-12, np.inf)
lrat = np.log(ratc)
prat = np.cumsum(lrat)[ie]
prat = np.exp(prat)
if freq_weights is None:
sprob += prat
else:
sprob += prat * freq_weights[i]
if freq_weights is None:
sprob /= n
return sprob, utime