timedep {lava} | R Documentation

## Time-dependent parameters

### Description

Add time-varying covariate effects to model

### Usage

    timedep(object, formula, rate, timecut, type = "coxExponential.lvm", ...)

### Arguments

`object` | Model
`formula` | Formula with rhs specifying time-varying covariates
`rate` | Optional rate parameters. If given as a vector this parameter is interpreted as the raw (baseline-)rates within each time interval defined by `timecut`. If given as a matrix the parameters are interpreted as log-rates (and log-rate-ratios for the time-varying covariates defined in the formula).
`timecut` | Time intervals
`type` | Type of model (default piecewise constant intensity)
`...` | Additional arguments to lower level functions

### Author(s)

Klaus K. Holst

### Examples

    ## Piecewise constant hazard
    m <- lvm(y~1)
    m <- timedep(m,y~1,timecut=c(0,5),rate=c(0.5,0.3))

    ## Not run:
    d <- sim(m,1e4); d$status <- TRUE
    dd <- mets::lifetable(Surv(y,status)~1,data=d,breaks=c(0,5,10));
    exp(coef(glm(events ~ offset(log(atrisk)) + -1 + interval, dd, family=poisson)))

    ## End(Not run)

    ## Piecewise constant hazard and time-varying effect of z1
    m <- lvm(y~1)
    distribution(m,~z1) <- ones.lvm(0.5)
    R <- log(cbind(c(0.2,0.7,0.9),c(0.5,0.3,0.3)))
    m <- timedep(m,y~z1,timecut=c(0,3,5),rate=R)

    ## Not run:
    d <- sim(m,1e4); d$status <- TRUE
    dd <- mets::lifetable(Surv(y,status)~z1,data=d,breaks=c(0,3,5,Inf));
    exp(coef(glm(events ~ offset(log(atrisk)) + -1 + interval+z1:interval, dd, family=poisson)))

    ## End(Not run)

    ## Explicit simulation of time-varying effects
    m <- lvm(y~1)
    distribution(m,~z1) <- ones.lvm(0.5)
    distribution(m,~z2) <- binomial.lvm(p=0.5)
    #variance(m,~m1+m2) <- 0
    #regression(m,m1[m1:0] ~ z1) <- log(0.5)
    #regression(m,m2[m2:0] ~ z1) <- log(0.3)
    regression(m,m1 ~ z1,variance=0) <- log(0.5)
    regression(m,m2 ~ z1,variance=0) <- log(0.3)
    intercept(m,~m1+m2) <- c(-0.5,0)
    m <- timedep(m,y~m1+m2,timecut=c(0,5))

    ## Not run:
    d <- sim(m,1e5); d$status <- TRUE
    dd <- mets::lifetable(Surv(y,status)~z1,data=d,breaks=c(0,5,Inf))
    exp(coef(glm(events ~ offset(log(atrisk)) + -1 + interval + interval:z1, dd, family=poisson)))

    ## End(Not run)

[Package lava version 1.5.1 Index]