fill_missing: Replacing missing measurement times in single-case data

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

Replacing missing measurement times in single-case data

Description

The fillmissing() function replaces missing measurements in single-case data.

Usage

fill_missing(data, dvar, mvar, na.rm = 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.
na.rm	If set TRUE, NA values are also interpolated. Default is na.rm = TRUE.

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 (overlap()) or a randomization test (rand_test()).

Value

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

Author(s)

Juergen Wilbert

See Also

Other data manipulation functions: add_l2(), as.data.frame.scdf(), as_scdf(), moving_median(), outlier(), ranks(), 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")
plot(study)

## Fill missing values in a single-case dataset that are NA
Maggie <- random_scdf(design(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")

scan documentation built on Aug. 8, 2023, 5:07 p.m.