evalstep: Evaluate step function at a set of new time points
In dynpred: Companion Package to "Dynamic Prediction in Clinical Survival Analysis"

Description Usage Arguments Details Value Author(s) Examples

Given one or more right-continuous step functions of time, given by vector time and vector of matrix stepf, this function evaluates the step function(s) at a vector of new time points given by newtime. Typical application is when the step function is given by a non- or semi-parametric estimated of cumulative hazard or survival function, and the value of this function is required at a set of time points.

1	evalstep(time, stepf, newtime, subst = -Inf, to.data.frame = FALSE)

`time`	A vector of time points at which the step function changes value
`stepf`	A vector (of the same length as `time`) or a matrix (with no of columns equal to the length of `time`) containing the values of the step function(s) at the time points
`newtime`	A vector of time points at which the step function(s) is/are to be evaluated
`subst`	A value that is substituted for elements of `newtime` that are smaller than the minimum of `time`. Default value is `-Inf`
`to.data.frame`	Determines whether the output is a data frame with the new time points and the values of the step function(s) (if `TRUE`) or a vector/matrix with the values of the step function(s) (if `FALSE` (default))

The argument time should be ordered, and not contain duplicated or +/- Inf, and should be of the same length as stepf. There are no restrictions on ordering or duplicates of newtime. For elements of newtime that are smaller than the minimum of time, the value of subst is substituted.

Either a vector/matrix containing the step function(s) evaluated at the new time points (if to.data.frame=FALSE (default)), or a data frame with column vectors newtime containing the new time points and res containing the step function evaluated at the new time points (if to.data.frame=TRUE)

Hein Putter H.Putter@lumc.nl

tm <- c(0.2,0.5,1,1.2,1.8,4)
ta <- 2*tm
data.frame(time=tm, stepf=ta)
evalstep(time=tm, stepf=ta, newtime=c(0,0.2,0.3,0.6,1,1.5,3,4,5,0.1), subst=0)
evalstep(time=tm, stepf=data.frame(ta=ta,ta2=1/ta),
	newtime=c(0,0.2,0.3,0.6,1,1.5,3,4,5,0.1), subst=0)