MannWhitneyCopula (version 0.1.1)

MW.plot: Plot method for Mann-Whitney effect under parametric copula models

Description

MW.plot plots the results, the parametric estimator and their confidence intervals, for the Mann-Whitney effect under parametric survival functions and copulas.

Usage

MW.plot(t.event, event, group,
copula = c("clayton", "gumbel", "frank", "fgm", "gb"),
lower = 0, upper = 1,
s1 = c("exponential", "weibull", "gamma", "log-normal", "burr3"),
s2 = c("exponential", "weibull", "gamma", "log-normal", "burr3"),
par1 = c(0, 0),
par2 = c(0, 0),
alpha = 0.05,
logit = FALSE,
xaxis = 2
)

Value

No return value, called for side effects (generates a plot).

Arguments

t.event

a vector for time-to-event.

event

a vector for event indicator.

group

a vector for group indicator.

copula

copula family.

lower

the lower end points of the interval of the copula parameter.

upper

the lower end points of the interval of the copula parameter.

s1

a parametric survival function for S1. Available options include; "exponential", "weibull", "gamma", "log-normal", "burr3".

s2

a parametric survival function for S2. Available options include; "exponential", "weibull", "gamma", "log-normal", "burr3".

par1

initial value(s) for the parameters of S1.

par2

initial value(s) for the parameters of S2.

alpha

significance level.

logit

logical; if TRUE, the estimator and CI is logit-transformed.

xaxis

a indicator specifying whether the xaxis is the copula parameter (xaxis = 1) or Kendall's tau (xaxis = 2).

Examples

##Exponential distributions
#set distribution parameter
lambda1 = 1
lambda2 = 2

#generate time to event
u = runif(100)
t.event1 = -log(u) / lambda1
t.event1 = sort(t.event1)

v = runif(100)
t.event2 = -log(v) / lambda2
t.event2 = sort(t.event2)

#censoring indicator
t1c = runif(100, 0, 1.5)
t.event1 = (t1c >= t.event1) * t.event1 + (t1c < t.event1) * t1c
event1 = 1 * (t1c > t.event1)

t2c = runif(100, 0, 0.8)
t.event2 = (t2c >= t.event2) * t.event2 + (t2c < t.event2) * t2c
event2 = 1 * (t2c > t.event2)

t.event = c(t.event1, t.event2)
event = c(event1, event2)

#group indicator
group = rep(c(1, 0), each = 100)

MW.plot(t.event, event, group,
          copula = "clayton",
          lower = 0.2, upper = 0.8,
          s1 = "exponential", s2 = "exponential",
          par1 = c(0, 0), par2 = c(0, 0), alpha = 0.05, logit = FALSE, xaxis = 2)