Create longitudinal/panel data
addPeriods(
dtName,
nPeriods = NULL,
idvars = "id",
timevars = NULL,
timevarName = "timevar",
timeid = "timeID",
perName = "period",
periodVec = NULL
)Name of existing data table
Number of time periods for each record
Names of index variables (in a string vector) that will be repeated during each time period
Names of time dependent variables. Defaults to NULL.
Name of new time dependent variable
Variable name for new index field. Defaults to "timevar"
Variable name for period field. Defaults to "period"
Vector of period times. Defaults to NULL
An updated data.table that that has multiple rows per observation in dtName
It is possible to generate longitudinal data with varying numbers of measurement periods as well as varying time intervals between each measurement period. This is done by defining specific variables in the data set that define the number of observations per subject and the average interval time between each observation. nCount defines the number of measurements for an individual; mInterval specifies the average time between intervals for a subject; and vInterval specifies the variance of those interval times. If mInterval is not defined, no intervals are used. If vInterval is set to 0 or is not defined, the interval for a subject is determined entirely by the mean interval. If vInterval is greater than 0, time intervals are generated using a gamma distribution with specified mean and dispersion. If either nPeriods or timevars is specified, that will override any nCount, mInterval, and vInterval data.
periodVec is used to specify measurement periods that are different the default counting variables. If periodVec is not specified, the periods default to 0, 1, ... n-1, with n periods. If periodVec is specified as c(x_1, x_2, ... x_n), then x_1, x_2, ... x_n represent the measurement periods.
tdef <- defData(varname = "T", dist = "binary", formula = 0.5)
tdef <- defData(tdef, varname = "Y0", dist = "normal", formula = 10, variance = 1)
tdef <- defData(tdef, varname = "Y1", dist = "normal", formula = "Y0 + 5 + 5 * T", variance = 1)
tdef <- defData(tdef, varname = "Y2", dist = "normal", formula = "Y0 + 10 + 5 * T", variance = 1)
dtTrial <- genData(5, tdef)
dtTrial
#> Key: <id>
#> id T Y0 Y1 Y2
#> <int> <int> <num> <num> <num>
#> 1: 1 1 11.41055 20.48760 27.10624
#> 2: 2 0 10.57090 16.28004 18.22835
#> 3: 3 0 11.05450 16.02915 21.83916
#> 4: 4 0 10.66699 16.46587 21.53544
#> 5: 5 0 10.40096 13.54061 21.05604
dtTime <- addPeriods(dtTrial,
nPeriods = 3, idvars = "id",
timevars = c("Y0", "Y1", "Y2"), timevarName = "Y"
)
dtTime
#> Key: <timeID>
#> id period T Y timeID
#> <int> <int> <int> <num> <int>
#> 1: 1 0 1 11.41055 1
#> 2: 1 1 1 20.48760 2
#> 3: 1 2 1 27.10624 3
#> 4: 2 0 0 10.57090 4
#> 5: 2 1 0 16.28004 5
#> 6: 2 2 0 18.22835 6
#> 7: 3 0 0 11.05450 7
#> 8: 3 1 0 16.02915 8
#> 9: 3 2 0 21.83916 9
#> 10: 4 0 0 10.66699 10
#> 11: 4 1 0 16.46587 11
#> 12: 4 2 0 21.53544 12
#> 13: 5 0 0 10.40096 13
#> 14: 5 1 0 13.54061 14
#> 15: 5 2 0 21.05604 15
# Varying # of periods and intervals - need to have variables
# called nCount and mInterval
def <- defData(varname = "xbase", dist = "normal", formula = 20, variance = 3)
def <- defData(def, varname = "nCount", dist = "noZeroPoisson", formula = 6)
def <- defData(def, varname = "mInterval", dist = "gamma", formula = 30, variance = .01)
def <- defData(def, varname = "vInterval", dist = "nonrandom", formula = .07)
dt <- genData(200, def)
dt[id %in% c(8, 121)]
#> Key: <id>
#> id xbase nCount mInterval vInterval
#> <int> <num> <num> <num> <num>
#> 1: 8 19.21504 4 27.55155 0.07
#> 2: 121 20.31966 3 34.23716 0.07
dtPeriod <- addPeriods(dt)
dtPeriod[id %in% c(8, 121)] # View individuals 8 and 121 only
#> Key: <timeID>
#> id period xbase time timeID
#> <int> <int> <num> <num> <int>
#> 1: 8 0 19.21504 0 41
#> 2: 8 1 19.21504 36 42
#> 3: 8 2 19.21504 62 43
#> 4: 8 3 19.21504 84 44
#> 5: 121 0 20.31966 0 734
#> 6: 121 1 20.31966 36 735
#> 7: 121 2 20.31966 64 736