smoothSC: Smoothing single-case data
In scan: Single-Case Data Analyses for Single and Multiple Baseline Designs

Description Usage Arguments Value Author(s) See Also Examples

The smoothSC function provides procedures to smooth single-case data (i.e., to eliminate noise). A moving average function (mean- or median-based) replaces each data point by the average of the surrounding data points step-by-step. With a local regression function, each data point is regressed by its surrounding data points.

1	smoothSC(data, dvar, mvar, FUN = "movingMedian", intensity = NULL)

`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.
`FUN`	Function determining the smoothed scores. Default `FUN = "movingMedian"` is a moving Median function. Further possible values are: `"movingMean"` and a non-parametric `"localRegression"`.
`intensity`	For `FUN = "movingMedian"` and `"movingMean"` it is the lag used for computing the average. Default is `intensity = 1`. In case of `FUN = "localRegression"` it is the proportion of surrounding data influencing each data point, which is `intensity = 0.2` by default.

Returns a data frame (for each single-case) with smoothed data points. See scdf to learn about the format of these data frames.

Juergen Wilbert

Other data manipulation functions: fillmissingSC(), longSCDF(), outlierSC(), rankSC(), scaleSC(), shiftSC(), truncateSC()

## Use the three different smoothing functions and compare the results
study <- c(
 "Original"         = Huber2014$Berta,
 "Moving Median"    = smoothSC(Huber2014$Berta, FUN = "movingMedian"),
 "Moving Mean"      = smoothSC(Huber2014$Berta, FUN = "movingMean"),
 "Local Regression" = smoothSC(Huber2014$Berta, FUN = "localRegression")
)
plot(study)