Modeling Survival Data Using Frailty Models
chap 2. Some Parametric Methods
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2 Exponential Distribution . . . . . . . . . . . . . . . . . . . 20
2.3 Weibull Distribution . . . . . . . . . . . . . . . . . . . . . 21
2.4 Extreme Value Distributions . . . . . . . . . . . . . . . . 23
2.5 Lognormal . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.6 Gamma . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.7 Loglogistic . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.8 Maximum Likelihood Estimation . . . . . . . . . . . . . 30
2.9 Parametric Regression Models
chap 6. Estimation Methods for Shared Frailty Models
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . .105
6.2 Inference for the Shared Frailty Model . . . . . . . . . . 106
6.3 The EM Algorithm . . . . . . . . . . . . . . . . . . . . . . .108
6.4 The Gamma Frailty Model . . . . . . . . . . . . . . . . . . . 110
6.5 The Positive Stable Frailty Model . . . . . . . . . . . . . . 111
6.6 The Lognormal Frailty Model . . . . . . . . . . . . . . . . . 113
6.6.1 Application to Seizure Data . . . . . . . . . . . . . . . 113
6.7 Modified EM (MEM) Algorithm for Gamma Frailty Models 114
6.8 Application
然后用最基本的package "survival"
并参考你的模型可能用到的一些functions:
survreg(formula, data, weights, subset,na.action, dist="weibull",....)
survreg.distributions include "weibull", "exponential", "gaussian",
"logistic","lognormal" and "loglogistic"
frailty(x, distribution="gamma", ...)
distribution: either the gamma, gaussian or t distribution may be specified.
frailty.gamma(x, sparse = (nclass >5), theta, df, eps = 1e-05,
method = c("em","aic", "df", "fixed"),...)
我是用的pscl包，zeroinfl()函数零膨胀负二项模型（ZINB）mod <- zeroinfl(ReportedNumber~ A+B+C+D+E | F+G+H+I, data = zinb, dist = "negbin", EM = TRUE)ZINB模型由点模型和零膨胀模型两部分结合而成，ABCDE是点模型内变量， 影响因变量发生次数的多少，FGHI是零膨胀模型内变量，决定因变量是否能够发生（为0还是非0）。 http://www.ats.ucla.edu/stat/r/dae/zinbreg.htm 这个网站里讲的很清楚
