scan: smooth_cases – R documentation

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smooth_cases

Smoothing single-case data

Description

The smooth_cases 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.

Usage

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

smoothSC(...)

Arguments

`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.
`...`	Further arguments passed to the function.

Value

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

Author(s)

Juergen Wilbert

Examples

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

scan

Single-Case Data Analyses for Single and Multiple Baseline Designs

v0.51

GPL

Authors

Juergen Wilbert [aut, cre], Timo Lueke [aut]

Initial release

2021-2-11