ftsa (version 2.2)

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

Description

Plot fitted model components for a fts object.

Usage

plot.ftsf(x, plot.type = c("function", "components", "variance"), 
 components, xlab1 = fit$y$xname, ylab1 = "Principal component", 
  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,...)

Value

Function produces a plot.

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.

References

R. J. Hyndman and M. S. Ullah (2007) "Robust forecasting of mortality and fertility rates: A functional data approach", Computational Statistics & 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.

See Also

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

Examples

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