scan (version 0.51)

fill_missing: Replacing missing measurement times in single-case data

Description

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

Usage

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

Arguments

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.

...

Further arguments passed to the function.

Value

A single-case data frame (SCDF) with missing data points interpolated. See scdf to learn about the SCDF Format.

Details

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).

See Also

Other data manipulation functions: longSCDF(), outlier(), ranks(), shift(), smooth_cases(), standardize(), truncate_phase()

Examples

# NOT RUN {
## 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")

# }