Function for transformation of discrete survival times in censoring encoding. Prior this function the data has to be already transformed to long format. With this new generated variable, the discrete censoring process can be analysed instead of the discrete survival process. In discrete survival analysis this information is used to constructs weights for predictive evaluation measures. It is applicable in single event survival analysis.
dataCensoring(dataSetLong, respColumn, idColumn)
Original data in transformed long format .
Name of column of discrete survival response as character.
Name of column of identification number of persons as character.
The standard procedure is to use functions such as
dataLongCompRisks to augment the data set from short format to long format before using
Original data set as argument *dataSetLong*, but with added censoring process as first variable in column "yCens"
Thomas Welchowski [email protected]
Ludwig Fahrmeir, (1997), Discrete failure time models, LMU Sonderforschungsbereich 386, Paper 91, http://epub.ub.uni-muenchen.de/
W. A. Thompson Jr., (1977), On the Treatment of Grouped Observations in Life Studies, Biometrics, Vol. 33, No. 3
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library(pec) data(cost) head(cost) IntBorders <- 1:ceiling(max(cost$time)/30)*30 subCost <- cost [1:100, ] # Convert from days to months CostMonths <- contToDisc (dataSet=subCost, timeColumn="time", intervalLimits=IntBorders) head(CostMonths) # Convert to long format based on months CostMonthsLong <- dataLong (dataSet=CostMonths, timeColumn="timeDisc", censColumn="status") head(CostMonthsLong, 20) # Generate censoring process variable CostMonthsCensor <- dataCensoring (dataSetLong=CostMonthsLong, respColumn="y", idColumn="obj") head(CostMonthsCensor) tail(CostMonthsCensor [CostMonthsCensor$obj==1, ], 10) tail(CostMonthsCensor [CostMonthsCensor$obj==3, ], 10)
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