lsqr: The Least Squares Method via Householder Transformation

In TSSS: Time Series Analysis with State Space Model

The Least Squares Method via Householder Transformation

Description

Compute regression coefficients of the model with minimum AIC by the least squares method via Householder transformation.

Usage

lsqr(y, lag = NULL, period = 365, plot = TRUE, ...)

Arguments

y	a univariate time series.
lag	number of sine and cosine components. Default is \sqrt{n}, where n is the length of the time series y.
period	period of one cycle.
plot	logical. If TRUE (default), original data and fitted trigonometric polynomial are plotted.
...	graphical arguments passed to plot.lsqr.

Value

An object of class "lsqr", which is a list with the following components:

aic	AIC's of the model with order 0,\dots,k ( = 2lag + 1).
sigma2	residual variance of the model with order 0,\dots,k.
maice.order	order of minimum AIC.
regress	regression coefficients of the model.
tripoly	trigonometric polynomial.

References

Kitagawa, G. (2020) Introduction to Time Series Modeling with Applications in R. Chapman & Hall/CRC.

Examples

# The daily maximum temperatures in Tokyo
data(Temperature)
lsqr(Temperature, lag = 10)

TSSS documentation built on Sept. 29, 2023, 9:07 a.m.