ftsa (version 5.5)

plot.ftsm: Plot fitted model components for a functional time series model

Description

Plot showing the basis functions in the top row of plots and the coefficients in the bottom row of plots.

Usage

# S3 method for ftsm
plot(x, components, components.start = 0, xlab1 = x$y$xname, ylab1 = "Basis function", 
 xlab2 = "Time", ylab2 = "Coefficient", mean.lab = "Mean", 
  level.lab = "Level", main.title = "Main effects", 
   interaction.title = "Interaction", basiscol = 1, coeffcol = 1, 
    outlier.col = 2, outlier.pch = 19, outlier.cex = 0.5, ...)

Arguments

x

Output from ftsm.

components

Number of principal components to plot.

components.start

Plotting specified component.

xlab1

x-axis label for basis functions.

xlab2

x-axis label for coefficient time series.

ylab1

y-axis label for basis functions.

ylab2

y-axis label for coefficient time series.

mean.lab

Label for mean component.

level.lab

Label for level component.

main.title

Title for main effects.

interaction.title

Title for interaction terms.

basiscol

Colors for basis functions if plot.type="components".

coeffcol

Colors for time series coefficients if plot.type="components".

outlier.col

Colour for outlying years.

outlier.pch

Plotting character for outlying years.

outlier.cex

Size of plotting character for outlying years.

...

Plotting parameters.

Value

None. Function produces a plot.

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

# NOT RUN {
# plot different principal components.	
plot.ftsm(ftsm(y = ElNino_ERSST_region_1and2, order = 2), components = 2)
# }