discSurv (version 2.0.0)

contToDisc: Continuous to Discrete Transformation

Description

Discretizes continuous time variable into a specified grid of censored data for discrete survival analysis. It is a data preprocessing step, before the data can be extendend in long format and further analysed with discrete survival models.

Usage

contToDisc(
  dataShort,
  timeColumn,
  intervalLimits,
  equi = FALSE,
  timeAsFactor = FALSE
)

Arguments

dataShort

Original data in short format ("class data.frame").

timeColumn

Name of the column of discrete survival times ("character vector").

intervalLimits

Right interval borders ("numeric vector"), e. g. if the intervals are [0, a_1), [a_1, a_2), [a_2, a_max), then intervalLimits = c(a_1, a_2, a_max)

equi

Specifies if argument intervalLimits should be interpreted as number of equidistant intervals ("logical vector").

timeAsFactor

Specifies if the computed discrete time intervals should be converted to a categorical variable ("logical vector"). Default is FALSE. In the default settings the discret time intervals are treated as quantitative ("numeric vector").

Value

Gives the data set expanded with a first column "timeDisc". This column includes the discrete time intervals ("class factor").

References

tutzModelDiscdiscSurv

See Also

dataLong, dataLongTimeDep, dataLongCompRisks

Examples

# NOT RUN {
# Example copenhagen stroke study data
library(pec)
data(cost)
head(cost)

# Convert observed times to months
# Right borders of intervals [0, a_1), [a_1, a_2), ... , [a_{\max-1}, a_{\max})
IntBorders <- 1:ceiling(max(cost$time)/30)*30

# Select subsample
subCost <- cost [1:100, ]
CostMonths <- contToDisc(dataShort=subCost, timeColumn = "time", intervalLimits = IntBorders)
head(CostMonths)

# Select subsample giving number of equidistant intervals
CostMonths <- contToDisc(dataShort = subCost, timeColumn = "time", intervalLimits = 10, equi = TRUE)
head(CostMonths)

# }