fillmissingSC function replaces missing measurements in single-case data.
A single-case data frame or a list of single-case data frames. See
Alternative options not yet included. Default is
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 (
A single-case data frame (SCDF) with missing data points interpolated. See
scdf to learn about the SCDF Format.
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## 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) plotSC(c(Original = Grosche2011, Filled = Grosche2011filled)) ## Fill missing values in a single-case dataset that are NA Maggie <- rSC(d.level = 1.0) Maggie_n <- Maggie replace.positions <- c(10,16,18) Maggie_n[][replace.positions,"values"] <- NA Maggie_f <- fillmissingSC(Maggie_n) plotSC(c("original" = Maggie, "missing" = Maggie_n, "interpolated" = Maggie_f), marks = list(positions = replace.positions))
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