fill_missing: Replacing missing measurement points in single-case data

In scan: Single-Case Data Analyses for Single and Multiple Baseline Designs

Replacing missing measurement points in single-case data

Description

The fillmissing() function replaces missing measurements in single-case data. It linearly interpolates missing data points between two existing measurements for all variables except the measurement time and phase. The measurement time variable is filled with the missing time points. The phase variable is copied from the previous measurement time point. If mt values are missing (NA), they are also interpolated if interpolate_na = TRUE.

Usage

fill_missing(data, dvar, mvar, pvar, interpolate_na = TRUE)

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.
pvar	Character string with the name of the phase variable. Defaults to the attributes in the scdf file.
interpolate_na	If set TRUE, NA values in the measurement time variable are also interpolated. Default is TRUE.

Details

The fill_missing() function is designed to handle single-case data with missing measurement points. It performs linear interpolation to estimate the missing values based on the existing data points. The function iterates through each single-case in the provided single-case data frame (scdf) and identifies gaps in the measurement time variable. For each gap, it calculates the step size for linear interpolation and fills in the missing values for all target variables (i.e., all variables except the measurement time and phase). The interpolated data points are then added to the single-case data frame, and the final result is sorted by measurement time. This function is particularly useful for preparing single-case data for further analysis, such as calculating overlap indices or conducting randomization tests, where continuous measurement times are required. It ensures that the data is complete by filling in the missing measurement points in a systematic manner.

Value

A single-case data frame with interpolated missing data points.

Author(s)

Juergen Wilbert

See Also

Other data manipulation functions: add_l2(), as.data.frame.scdf(), as_scdf(), batch_apply(), moving_median(), print.sc_outlier(), ranks(), rescale(), scdf(), select_cases(), set_vars(), shift(), smooth_cases(), standardize(), truncate_phase()

Examples

## 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")
study

## An example with multiple interpolated variables

rolf_n <- exampleAB_add
rolf_n[[1]] <- rolf_n[[1]][-c(3,7,8),]
rolf_f <- fill_missing(rolf_n)
study1 <- c("original" = exampleAB_add, "interpolated" = rolf_f)
study1

## Example with missing NAs in measurement time
Maggie2 <- random_scdf(design(level = list(0,1)), seed = 123)
Maggie2_n <- Maggie2
Maggie2_n[[1]][c(5,12:14,20), "mt"] <- NA
Maggie2_f <- fill_missing(Maggie2_n)
study2 <- c("original" = Maggie2, "interpolated" = Maggie2_f)
study2

scan documentation built on April 1, 2026, 9:06 a.m.