DC_F: Fluctuation Intensity

In FredHasselman/casnet: A Toolbox for Studying Complex Adaptive Systems and Networks

Fluctuation Intensity

Description

Fluctuation intensity is one of two components of which the product is the Dynamic Complexity measure.

Usage

dc_f(
  df,
  win = NROW(df),
  scale_min,
  scale_max,
  doPlot = FALSE,
  useVarNames = TRUE,
  colOrder = TRUE,
  useTimeVector = NA,
  timeStamp = "31-01-1999"
)

Arguments

df	A data frame containing multivariate time series data from 1 person. Rows should indicate time, columns should indicate the time series variables. All time series in df should be on the same scale, an error will be thrown if the range of the time series indf is not ⁠[scale_min,scale_max]⁠.
win	Size of window in which to calculate Dynamic Complexity. If win < NROW(df) the window will move along the time series with a stepsize of 1 (default = NROW(df))
scale_min	The theoretical minimum value of the scale. Used to calculate expected values, so it is important to set this to the correct value.
scale_max	The theoretical maximum value of the scale. Used to calculate expected values, so it is important to set this to the correct value.
doPlot	If TRUE shows a Complexity Resonance Diagram of the Dynamic Complexity and returns an invisible ggplot2::ggplot() object. (default = FALSE)
useVarNames	Use the column names of df as variable names in the Complexity Resonance Diagram (default = TRUE)
colOrder	If TRUE, the order of the columns in df determines the of variables on the y-axis. Use FALSE for alphabetic/numeric order. Use NA to sort by by mean value of Dynamic Complexity (default = TRUE)
useTimeVector	Parameter used for plotting. A vector of length NROW(df), containing date/time information (default = NA)
timeStamp	If useTimeVector is not NA, a character string that can be passed to lubridate::stamp() to format the the dates/times passed in useTimeVector (default = "01-01-1999")

Value

dataframe

Author(s)

Merlijn Olthof

Fred Hasselman

References

Haken H, & Schiepek G. (2006). Synergetik in der Psychologie. Selbstorganisation verstehen und gestalten. Hogrefe, Göttingen.

Schiepek, G. (2003). A Dynamic Systems Approach to Clinical Case Formulation. European Journal of Psychological Assessment, 19, 175-184. https://doi.org/10.1027//1015-5759.19.3.175

Schiepek, G., & Strunk, G. (2010). The identification of critical fluctuations and phase transitions in short term and coarse-grained time series-a method for the real-time monitoring of human change processes. Biological cybernetics, 102(3), 197-207. https://doi.org/10.1007/s00422-009-0362-1

See Also

Use dc_win() to get the dynamic complexity measure.

Other Dynamic Complexity functions: dc_ccp(), dc_d(), dc_win(), plotDC_ccp(), plotDC_lvl(), plotDC_res()

FredHasselman/casnet documentation built on March 10, 2024, 8:23 a.m.