residuals.fm: Compute residuals from a functional model

In ftsa: Functional Time Series Analysis

Compute residuals from a functional model

Description

After fitting a functional model, it is useful to inspect the residuals. This function extracts the relevant information from the fit object and puts it in a form suitable for plotting with image, persp, contour, filled.contour, etc.

Usage

## S3 method for class 'fm'
residuals(object, ...)

Arguments

object	Output from ftsm or fplsr.
...	Other arguments.

Value

Produces an object of class “fmres” containing the residuals from the model.

Author(s)

Rob J Hyndman

References

B. Erbas and R. J. Hyndman and D. M. Gertig (2007) "Forecasting age-specific breast cancer mortality using functional data model", Statistics in Medicine, 26(2), 458-470.

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.

H. L. Shang (2012) "Point and interval forecasts of age-specific fertility rates: a comparison of functional principal component methods", Journal of Population Research, 29(3), 249-267.

H. L. Shang (2012) "Point and interval forecasts of age-specific life expectancies: a model averaging", Demographic Research, 27, 593-644.

See Also

ftsm, forecast.ftsm, summary.fm, plot.fm, plot.fmres

Examples

plot(residuals(object = ftsm(y = ElNino_ERSST_region_1and2)), 
	xlab = "Year", ylab = "Month")

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