Handling outliers in single-case data
Identifies and drops outliers within a single-case data frame (scdf).
outlier(data, dvar, pvar, mvar, criteria = c("MAD", "3.5"))
outlierSC(...)data |
A single-case data frame. See |
dvar |
Character string with the name of the dependent 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. |
mvar |
Character string with the name of the measurement time variable. Defaults to the attributes in the scdf file. |
criteria |
Specifies the criteria for outlier identification. Set
|
... |
Further arguments passed to the function. |
data |
A single-case data frame with substituted outliers. |
dropped.n |
A list with the number of dropped data points for each single-case. |
dropped.mt |
A list with the measurement-times of dropped
data points for each single-case (values are based on the |
sd.matrix |
A list with a matrix for each case with values for the upper and lower boundaries based on the standard deviation. |
ci.matrix |
A list with a matrix for each single-case with values for the upper and lower boundaries based on the confidence interval. |
cook |
A list of Cook's Distances for each measurement of each single-case. |
criteria |
Criteria used for outlier analysis. |
N |
Number of single-cases. |
case.names |
Case identifier. |
Juergen Wilbert
Other data manipulation functions:
fill_missing(),
longSCDF(),
ranks(),
shift(),
smooth_cases(),
standardize(),
truncate_phase()
## Identify outliers using 1.5 standard deviations as criterion
susanne <- rSC(level = 1.0)
res_outlier <- outlier(susanne, criteria = c("SD", 1.5))
plot(susanne, marks = res_outlier)
## Identify outliers in the original data from Grosche (2011) using Cook's Distance
## greater than 4/n as criterion
res_outlier <- outlier(Grosche2011, criteria = c("Cook", "4/n"))
plot(Grosche2011, marks = res_outlier)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.