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ftsa (version 4.7)

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

"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, ...)

Arguments

x
Output from ftsm or fplsr.
order
Number of principal components to plot. Default is all principal components in a model.
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.
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.

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

Run this code
plot(x = ftsm(y = ElNino))

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