eig_win | R Documentation |
Calculate the eigenvalue of the first PCA component in a right-aligned sliding window on (multivariate) time series data.
eig_win(
df,
win = NROW(df),
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 |
doPlot |
If |
useVarNames |
Use the column names of |
colOrder |
If |
useTimeVector |
Parameter used for plotting. A vector of length |
timeStamp |
If |
Data frame with the eigenvalues in requested window size.
For different step-sizes or window alignments see ts_windower()
.
Merlijn Olthof
Fred Hasselman
data(ColouredNoise)
eig_win(df = elascer(ColouredNoise[,c(1,11,21,31,41)],groupwise = TRUE), win = 128, doPlot = TRUE)
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