pseudo.spectrum)
are converted into the MA coefficients of the model for each component
by means of acgf2poly.
canonical.decomposition(num.trend, den.trend, num.trans, den.trans, num.seas, den.seas, quotient, optim.tol = 1e-04, ...)
"print"(x, units = c("radians", "degrees", "pi"), digits = 4, ...)pseudo.spectrum.pseudo.spectrum.pseudo.spectrum.pseudo.spectrum.pseudo.spectrum.pseudo.spectrum.pseudo.spectrum.
(Different from zero only when
the degree of the MA polynomial is equal or higher than the degree of the AR polynomial
in the fitted model).optimize.units="pi" prints the
argument in radians as multiples of $pi$.tsdecCanDec returned by canonical.decomposition.print.tsdecCanDec containing
the MA coefficients of the ARIMA models obtained for the unobserved components
(e.g., trend, seasonal) and the variance of the corresponding disturbance terms.
Hillmer, S. C. and Tiao, G. C. (1982) An ARIMA-Model-Based Approach to Seasonal Adjustment. Journal of the American Statistical Association, 77(377), pp. 63-70. \Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("#1")}10.1080/01621459.1982.10477767http://doi.org/10.1080/01621459.1982.10477767doi:\ifelse{latex}{\out{~}}{ }latex~ 10.1080/01621459.1982.10477767 .
acgf2poly, pseudo.spectrum,
optimize.