lava (version 1.4.1)

curereg: Regression model for binomial data with unkown group of immortals

Description

Regression model for binomial data with unkown group of immortals

Usage

curereg(formula, cureformula = ~1, data, family = binomial(),
  offset = NULL, start, var = "hessian", ...)

Arguments

formula

Formula specifying

cureformula

Formula for model of disease prevalence

data

data frame

family

Distribution family (see the help page family)

offset

Optional offset

start

Optional starting values

var

Type of variance (robust, expected, hessian, outer)

...

Additional arguments to lower level functions

Examples

## Simulation
n <- 2e3
x <- runif(n,0,20)
age <- runif(n,10,30)
z0 <- rnorm(n,mean=-1+0.05*age)
z <- cut(z0,breaks=c(-Inf,-1,0,1,Inf))
p0 <- lava:::expit(model.matrix(~z+age) %*% c(-.4, -.4, 0.2, 2, -0.05))
y <- (runif(n)<lava:::tigol(-1+0.25*x-0*age))*1
u <- runif(n)<p0
y[u==0] <- 0
d <- data.frame(y=y,x=x,u=u*1,z=z,age=age)
head(d)

## Estimation
e0 <- curereg(y~x*z,~1+z+age,data=d)
e <- curereg(y~x,~1+z+age,data=d)
compare(e,e0)
e
PD(e0,intercept=c(1,3),slope=c(2,6))

B <- rbind(c(1,0,0,0,20),
           c(1,1,0,0,20),
           c(1,0,1,0,20),
           c(1,0,0,1,20))
prev <- summary(e,pr.contrast=B)$prevalence

x <- seq(0,100,length.out=100)
newdata <- expand.grid(x=x,age=20,z=levels(d$z))
fit <- predict(e,newdata=newdata)
plot(0,0,type="n",xlim=c(0,101),ylim=c(0,1),xlab="x",ylab="Probability(Event)")
count <- 0
for (i in levels(newdata$z)) {
  count <- count+1
  lines(x,fit[which(newdata$z==i)],col="darkblue",lty=count)
}
abline(h=prev[3:4,1],lty=3:4,col="gray")
abline(h=prev[3:4,2],lty=3:4,col="lightgray")
abline(h=prev[3:4,3],lty=3:4,col="lightgray")
legend("topleft",levels(d$z),col="darkblue",lty=seq_len(length(levels(d$z))))