plot.ftsf: 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 fitted model components for a fts object.

Usage

## S3 method for class 'ftsf'
plot(x, plot.type = c("function", "components", "variance"), 
 components, xlab1 = fit$y$xname, ylab1 = "Basis function", 
  xlab2 = "Time", ylab2 = "Coefficient", mean.lab = "Mean", 
   level.lab = "Level", main.title = "Main effects", 
    interaction.title = "Interaction", vcol = 1:3, shadecols = 7, 
     fcol = 4, basiscol = 1, coeffcol = 1, outlier.col = 2,
      outlier.pch = 19, outlier.cex = 0.5,...)

Arguments

x	Output from forecast.ftsm.
plot.type	Type of plot.
components	Number of principal components.
xlab1	x-axis label for principal components.
xlab2	x-axis label for coefficient time series.
ylab1	y-axis label for principal components.
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.
vcol	Colors to use if plot.type = "variance".
shadecols	Color for shading of prediction intervals when plot.type = "components".
fcol	Color of point forecasts when plot.type = "components".
basiscol	Colors for principal components if plot.type = "components".
coeffcol	Colors for time series coefficients if plot.type = "components".
outlier.col	Colors for outlying years.
outlier.pch	Plotting character for outlying years.
outlier.cex	Size of plotting character for outlying years.
...	Plotting parameters.

Details

When plot.type = "function", it produces a plot of the forecast functions;

When plot.type = "components", it produces a plot of the principla components and coefficients with forecasts and prediction intervals for each coefficient;

When plot.type = "variance", it produces a plot of the variance components.

Value

Function produces a plot.

Author(s)

Rob J Hyndman

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. Booth (2008) "Stochastic population forecasts using functional data models for mortality, fertility and migration", International Journal of Forecasting, 24(3), 323-342.

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

H. L. Shang, H. Booth and R. J. Hyndman (2011) "Point and interval forecasts of mortality rates and life expectancy: A comparison of ten principal component methods", Demographic Research, 25(5), 173-214.

See Also

ftsm, plot.fm, plot.fmres, residuals.fm, summary.fm

Examples

plot(x = forecast(object = ftsm(y = ElNino_ERSST_region_1and2)))

ftsa documentation built on May 29, 2024, 2:47 a.m.