Replacing missing measurement times in single-case data
The fillmissingSC function replaces missing measurements in
single-case data.
fill_missing(data, dvar, mvar, interpolation = "linear", na.rm = TRUE) fillmissingSC(...)
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 |
... |
Further arguments passed to the function. |
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(),
outlier(),
ranks(),
shift(),
smooth_cases(),
standardize(),
truncate_phase()
## 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 <- fill_missing(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 <- fill_missing(Maggie_n)
study <- c(Maggie, Maggie_n, Maggie_f)
names(study) <- c("original", "missing", "interpolated")
plot(study, marks = list(positions = replace.positions), style = "grid2")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.