ftsa (version 6.4)

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

Description

Plot fitted model components for a fts object.

Usage

# S3 method for 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,...)

Value

Function produces a plot.

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.

Author

Rob J Hyndman

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 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)))