ftsa (version 3.2)

plot.fm: Plot fitted model components for a functional model

Description

When class(x)[1] = ftsm, plot showing the principal components in the top row of plots and the coefficients in the bottom row of plots. When class(x)[1] = fm, plot showing the predictor scores in the top row of plots and the response loadings in the bottom row of plots.

Usage

## S3 method for class 'fm':
plot(x, order, xlab1 = x$y$xname, ylab1 = "Principal component", 
 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, ...)

Value

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

Examples

plot(x = ftsm(y = ElNino))
plot(x = fplsr(data = ElNino), ylab1 = "Predictor score", 
     ylab2 = "Response loading")