fGTDL {GTDL} | R Documentation |
The GTDL distribution
Description
Density function, survival function, failure function and random generation for the GTDL distribution.
Usage
dGTDL(t, param, log = FALSE)
hGTDL(t, param)
sGTDL(t, param)
rGTDL(n, param)
Arguments
t |
vector of integer positive quantile. |
param |
parameters (alpha and gamma are scalars, lambda non-negative). |
log |
logical; if TRUE, probabilities p are given as log(p). |
n |
number of observations. |
Details
Density function
Survival function
Failure function
Value
dGTDL
gives the density function, hGTDL
gives the failure function, sGTDL
gives the survival function and rGTDL
generates random samples.
Invalid arguments will return an error message.
Source
[d-p-q-r]GTDL are calculated directly from the definitions.
References
Mackenzie, G. (1996). Regression Models for Survival Data: The Generalized Time-Dependent Logistic Family. Journal of the Royal Statistical Society. Series D (The Statistician). 45. 21-34.
Examples
library(GTDL)
t <- seq(0,20,by = 0.1)
lambda <- 1.00
alpha <- -0.05
gamma <- -1.00
param <- c(lambda,alpha,gamma)
y1 <- hGTDL(t,param)
y2 <- sGTDL(t,param)
y3 <- dGTDL(t,param,log = FALSE)
tt <- as.matrix(cbind(t,t,t))
yy <- as.matrix(cbind(y1,y2,y3))
matplot(tt,yy,type="l",xlab="time",ylab="",lty = 1:3,col=1:3,lwd=2)
y1 <- hGTDL(t,c(1,0.5,-1.0))
y2 <- hGTDL(t,c(1,0.25,-1.0))
y3 <- hGTDL(t,c(1,-0.25,1.0))
y4 <- hGTDL(t,c(1,-0.50,1.0))
y5 <- hGTDL(t,c(1,-0.06,-1.6))
tt <- as.matrix(cbind(t,t,t,t,t))
yy <- as.matrix(cbind(y1,y2,y3,y4,y5))
matplot(tt,yy,type="l",xlab="time",ylab="Hazard function",lty = 1:3,col=1:3,lwd=2)