unimar {timsac} | R Documentation |
This is the basic program for the fitting of autoregressive models of successively higher by the method of least squares realized through householder transformation.
unimar(y, max.order=NULL, plot=FALSE, tmp.file=NULL)
y |
a univariate time series. |
max.order |
upper limit of AR order. Default is 2*sqrt(n), where n is the length of the time series y. |
plot |
logical. If TRUE daic is plotted. |
tmp.file |
a character string naming a file written intermediate results of AR coefficients computation. If NULL (default) output no file. |
The AR model is given by
y(t) = a(1)y(t-1) + .... + a(p)y(t-p) + u(t)
where p is AR order and u(t) is Gaussian white noise with mean 0 and variance v.
AIC is defined by
AIC = nlog(det(v)) + 2k
where n is the length of data, v is the estimates of the innovation variance and k is the number of parameter.
mean |
mean. |
var |
variance. |
v |
innovation variance. |
aic |
AIC(m) (m = 0,...,max.order). |
aicmin |
minimum AIC. |
daic |
AIC(m)-aicmin (m = 0,...,max.order). |
order.maice |
order of minimum AIC. |
v.maice |
innovation variance attained at "order.maice" . |
arcoef |
AR coefficients. |
G.Kitagawa and H.Akaike (1978) A Procedure For The Modeling of Non-Stationary Time Series. Ann. Inst. Statist. Math.,30, B, 351–363.
H.Akaike, G.Kitagawa, E.Arahata and F.Tada (1979) Computer Science Monograph, No.11, Timsac78. The Institute of Statistical Mathematics.
data(Canadianlynx) z <- unimar(Canadianlynx, max.order=20) z$arcoef