fillmissingSC: Replacing missing measurement times in single-case data
In scan: Single-Case Data Analyses for Single and Multiple Baseline Designs

Description Usage Arguments Details Value Author(s) See Also Examples

The fillmissingSC function replaces missing measurements in single-case data.

1	fillmissingSC(data, dvar, mvar, interpolation = "linear", na.rm = TRUE)

`data`	A single-case data frame. See `scdf` to learn about this format.
`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 `interpolation = "linear"`.
`na.rm`	If set `TRUE`, `NA` values are also interpolated. Default is `na.rm = TRUE`.

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")