plotfplsr: Plot fitted model components for a functional time series...

In ftsa: Functional Time Series Analysis

Plot fitted model components for a functional time series model

Description

Plot showing the basis functions of the predictors in the top row, followed by the basis functions of the responses in the second row, then the coefficients in the bottom row of plots.

Usage

plotfplsr(x, xlab1 = x$ypred$xname, ylab1 = "Basis function", xlab2 = "Time", 
 ylab2 = "Coefficient", mean.lab = "Mean", interaction.title = "Interaction")

Arguments

x	Output from fplsr.
xlab1	x-axis label for basis functions.
ylab1	y-axis label for basis functions.
xlab2	x-axis label for coefficient time series.
ylab2	y-axis label for coefficient time series.
mean.lab	Label for mean component.
interaction.title	Title for interaction terms.

Value

None. Function produces a plot.

Author(s)

Han Lin Shang

References

R. J. Hyndman and M. S. Ullah (2007) "Robust forecasting of mortality and fertility rates: A functional data approach", Computational Statistics and Data Analysis, 51(10), 4942-4956.

R. J. Hyndman and H. L. Shang (2009) "Forecasting functional time series" (with discussion), Journal of the Korean Statistical Society, 38(3), 199-221.

See Also

forecast.ftsm, ftsm, plot.fm, plot.ftsf, residuals.fm, summary.fm

Examples

# Fit the data by the functional partial least squares.	
ausfplsr = fplsr(data = ElNino_ERSST_region_1and2, order = 2)
plotfplsr(x = ausfplsr)

ftsa documentation built on Sept. 11, 2023, 5:09 p.m.