fts.plot.filters: Plot kernels

In kidzik/freqdom.fda: Functional Time Series: Dynamic Functional Principal Components

Plot kernels

Description

Plot kernels

Usage

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)

kidzik/freqdom.fda documentation built on April 19, 2022, 2:40 a.m.