polreg {TSSS} | R Documentation |
Estimate the trend using the AIC best polynomial regression model.
polreg(y, order, plot = TRUE, ...)
y |
a univariate time series. |
order |
order of polynomial regression. |
plot |
logical. If |
... |
further arguments to be passed to plot.polreg. |
An object of class "polreg"
, which is a list with the following
elements:
order.maice |
MAICE (minimum AIC estimate) order. |
sigma2 |
residual variance of the model with order M.
(0 <= M <= |
aic |
AIC of the model with order M.
(0 <= M <= |
daic |
AIC - minimum AIC. |
coef |
regression coefficients A(I,M) with order M. (1 <= M <= |
trend |
trend component. |
Kitagawa, G. (2010) Introduction to Time Series Modeling. Chapman & Hall/CRC.
# The daily maximum temperatures for Tokyo data(Temperature) polreg(Temperature, order = 7) # Wholesale hardware data data(WHARD) y <- log10(WHARD) polreg(y, order = 15)