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The fillmissing() 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 (overlap()) or a
randomization test (rand_test()).
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()
## 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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