fts.plot.filters function

Plot kernels

fts.plot.filters(A, Ndpc = 1, lags = -3:3, one.plot = FALSE, ...)

Arguments

• A: a functional filter sequence given as object of class fts.timedom.
• Ndpc: if Ndpc = k the first k filter sequences are plotted.
• lags: number of lags to plot.
• one.plot: if TRUE then functional filters corresponding belonging to the respective scores will all be plotted in the same graph.
• ...: arguments col, lwd, lty passed to plot

Examples

# Load example PM10 data from Graz, Austria
data(pm10) # loads functional time series pm10 to the environment
X = center.fd(pm10)

# Compute functional dynamic principal components with only one component
res.dpca = fts.dpca(X, Ndpc = 1, freq=(-25:25/25)*pi) # leave default freq for higher precision

# Plot Functional Dynamic Principal Component Filters
fts.plot.filters(res.dpca$filters)