MannWhitneyCopula (version 0.1.1)

survival.mle: Maximum likelihood estimation under parametric survival function

Description

survival.mle provides the maximum likelihood estimator and their variance-covariance matrix under parametric survival functions. This function also provides AIC and Kolmogorov-Smirnov distance to evaluate the model fitting.

Usage

survival.mle(
t.event,
event,
distribution = c("exponential", "weibull", "gamma", "log-normal", "burr3"),
par = c(0,0))

Value

estimate: a vector of MLE.

var: variance-covariance matrix of MLE.

AIC: Akaike information criteria.

KS: Kolmogorov-Smirnov distance between MLE and KM estimator.

Arguments

t.event

a vector for time-to-event.

event

a vector for event indicator.

distribution

a parametric distribution for survival function. Available options include; "exponential", "weibull", "gamma", "log-normal", "burr3".

par

initial value(s) for the distribution parameters.

Examples

#MLE under exponential distribution
#set distribution parameter
lambda = 1

#generate time to event
u = runif(100)
t.event = -log(u)/lambda
t.event = sort(t.event)

#censoring indicator
tc = runif(100, 0, 1.5)
t.event = (tc >= t.event) * t.event + (tc < t.event) * tc
event = 1 * (tc > t.event)

survival.mle(t.event, event, distribution = "exponential")