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The fillmissingSC
function replaces missing measurements in
single-case data.
fill_missing(data, dvar, mvar, na.rm = TRUE)
A single-case data frame with interpolated missing data
points. See scdf
to learn about the SCDF Format.
A single-case data frame. See scdf
to learn about
this format.
Character string with the name of the dependent variable. Defaults to the attributes in the scdf file.
Character string with the name of the measurement time variable. Defaults to the attributes in the scdf file.
If set TRUE
, NA
values are also interpolated.
Default is na.rm = TRUE
.
Juergen Wilbert
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
).
Other data manipulation functions:
as.data.frame.scdf()
,
outlier()
,
ranks()
,
shift()
,
smooth_cases()
,
standardize()
,
truncate_phase()
## 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")
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