dc_f | R Documentation |
Fluctuation intensity is one of two components of which the product is the Dynamic Complexity measure.
dc_f(
df,
win = NROW(df),
scale_min,
scale_max,
doPlot = FALSE,
useVarNames = TRUE,
colOrder = TRUE,
useTimeVector = NA,
timeStamp = "31-01-1999"
)
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 |
win |
Size of window in which to calculate Dynamic Complexity. If |
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 |
useVarNames |
Use the column names of |
colOrder |
If |
useTimeVector |
Parameter used for plotting. A vector of length |
timeStamp |
If |
dataframe
Merlijn Olthof
Fred Hasselman
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
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()
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.