ctsem (version 2.8.2)

ctIntervalise: Converts absolute times to intervals for wide format ctsem panel data

Description

Converts absolute times to intervals for wide format ctsem panel data

Usage

ctIntervalise(datawide, Tpoints, n.manifest, n.TDpred = 0,
  n.TIpred = 0, imputedefs = F, manifestNames = "auto",
  TDpredNames = "auto", TIpredNames = "auto", digits = 5,
  mininterval = 0.001, individualRelativeTime = TRUE,
  startoffset = 0)

Arguments

datawide

Wide format data, containing absolute time measurements, to convert to interval time scale. Otherwise as used in ctFit. See ctLongToWide to easily convert long format data.

Tpoints

Maximum number of discrete time points (waves of data, or measurement occasions) for an individual in the input data structure.

n.manifest

number of manifest variables per time point in the data.

n.TDpred

number of time dependent predictors in the data structure.

n.TIpred

number of time independent predictors in the data structure.

imputedefs

if TRUE, impute time intervals based on the measurement occasion (i.e. column) they are in, if FALSE (default), set related observations to NA. FALSE is recommended unless you are certain that the imputed value (mean of the relevant time column) is appropriate. Noise and bias in estimates will result if wrongly set to TRUE.

manifestNames

vector of character strings giving variable names of manifest indicator variables (without _Tx suffix for measurement occasion).

TDpredNames

vector of character strings giving variable names of time dependent predictor variables (without _Tx suffix for measurement occasion).

TIpredNames

vector of character strings giving variable names of time independent predictor variables.

digits

How many digits to round to for interval calculations.

mininterval

set to lower than any possible observed measurement interval, but above 0 - this is used for filling NA values where necessary and has no impact on estimates when set in the correct range. (If all observed intervals are greater than 1, mininterval=1 may be a good choice)

individualRelativeTime

if TRUE (default), the first measurement for each individual is assumed to be taken at time 0, and all other times are adjusted accordingly. If FALSE, new columns for an initial wave are created, consisting only of observations which occurred at the earliest observation time of the entire sample.

startoffset

if 0 (default) uses earliest observation as start time. If greater than 0, all first observations are NA, with distance of startoffset to first recorded observation.

Details

Time column must be numeric!

Examples

# NOT RUN {
#First load the long format data with absolute times
data('longexample')

#Then convert to wide format
wideexample <- ctLongToWide(datalong = longexample, id = "id", 
time = "time", manifestNames = c("Y1", "Y2", "Y3"), 
TDpredNames = "TD1", TIpredNames = c("TI1", "TI2"))

#Then convert the absolute times to intervals, using the Tpoints reported from the prior step.
wide <- ctIntervalise(datawide = wideexample, Tpoints = 4, n.manifest = 3, 
n.TDpred = 1, n.TIpred = 2, manifestNames = c("Y1", "Y2", "Y3"), 
TDpredNames = "TD1", TIpredNames = c("TI1", "TI2") )
 
print(wide)
# }