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

In lava: Latent Variable Models

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

Author(s)

Klaus K. Holst

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))))

lava documentation built on May 2, 2019, 4:49 p.m.