fillmissingSC {scan} | R Documentation |
The fillmissingSC
function replaces missing measurements in
single-case data.
fillmissingSC(data, dvar, mvar, interpolation = "linear", na.rm = TRUE)
data |
A single-case data frame. See |
dvar |
Character string with the name of the dependent variable. Defaults to the attributes in the scdf file. |
mvar |
Character string with the name of the measurement time variable. Defaults to the attributes in the scdf file. |
interpolation |
Alternative options not yet included. Default is
|
na.rm |
If set |
This procedure is recommended if there are gaps between measurement times
(e.g. MT: 1, 2, 3, 4, 5, ... 8, 9) or explicitly missing values in your
single-case data and you want to calculate overlap indices
(overlapSC
) or a randomization test (randSC
).
A single-case data frame (SCDF) with missing data points
interpolated. See scdf
to learn about the SCDF Format.
Juergen Wilbert
Other data manipulation functions:
longSCDF()
,
outlierSC()
,
rankSC()
,
scaleSC()
,
shiftSC()
,
smoothSC()
,
truncateSC()
## In his study, Grosche (2011) could not realize measurements each single week for ## all participants. During the course of 100 weeks, about 20 measurements per person ## at different times were administered. ## Fill missing values in a single-case dataset with discontinuous measurement times Grosche2011filled <- fillmissingSC(Grosche2011) study <- c(Grosche2011[2], Grosche2011filled[2]) names(study) <- c("Original", "Filled") plot(study, style = "grid") ## Fill missing values in a single-case dataset that are NA Maggie <- rSC(design_rSC(level = list(0,1)), seed = 123) Maggie_n <- Maggie replace.positions <- c(10,16,18) Maggie_n[[1]][replace.positions,"values"] <- NA Maggie_f <- fillmissingSC(Maggie_n) study <- c(Maggie, Maggie_n, Maggie_f) names(study) <- c("original", "missing", "interpolated") plot(study, marks = list(positions = replace.positions), style = "grid2")