x12 (version 1.10.3)

summary-methods: ~~ Methods for Function summary in Package x12 ~~

Description

Delivers a diagnostics summary for x12 output.

Usage

# S4 method for x12Output
summary(object, fullSummary=FALSE, spectra.detail=FALSE,
  almostout=FALSE, rsd.autocorr=NULL,
  quality.stat=FALSE, likelihood.stat=FALSE, aape=FALSE, id.rsdseas=FALSE,
  slidingspans=FALSE,
  history=FALSE, identify=FALSE, print=TRUE)
# S4 method for x12Single
summary(object, fullSummary=FALSE, spectra.detail=FALSE,
  almostout=FALSE, rsd.autocorr=NULL,
  quality.stat=FALSE, likelihood.stat=FALSE, aape=FALSE, id.rsdseas=FALSE,
  slidingspans=FALSE,
  history=FALSE, identify=FALSE, oldOutput=NULL,print=TRUE)                      
# S4 method for x12Batch
summary(object, fullSummary=FALSE, spectra.detail=FALSE,
  almostout=FALSE, rsd.autocorr=NULL,
  quality.stat=FALSE, likelihood.stat=FALSE, aape=FALSE, id.rsdseas=FALSE,
  slidingspans=FALSE,
  history=FALSE, identify=FALSE, oldOutput=NULL,print=TRUE)

Arguments

object

object of class x12Output-class, x12Single-class or x12Batch-class.

fullSummary

logical defining whether all available optional diagnostics below should be included in the summary.

spectra.detail

logical defining whether more detail on the spectra should be returned.

almostout

logical defining whether "almost" outliers should be returned.

rsd.autocorr

character or character vector specifying the type of autocorrelation of the residuals that should be returned, i.e. the autocorrelations and/or partial autocorrelations of the residuals and/or the autocorrelations of the squared residuals ("acf", "pacf", "acf2").

quality.stat

logical defining whether the second Q statistic, i.e. the Q Statistic computed w/o the M2 Quality Control Statistic, and the M statistics for monitoring and quality assessment should be returned as well.

likelihood.stat

if TRUE, the likelihood statistics AIC, AICC, BIC and HQ are returned as well as the estimated maximum value of the log likelihood function of the model for the untransformed data.

aape

logical defining whether the average absolute percentage error for forecasts should be returned.

id.rsdseas

logical defining whether the presence/absence of residual seasonality should be indicated.

slidingspans

logical defining whether the diagnostics output of the slidingspans analysis should be returned.

history

logical defining whether the diagnostics output of the (revision) history analysis should be returned.

identify

logical defining whether the (partial) autocorrelations of the residuals generated by the "identify" specification should be returned.

oldOutput

integer specifying the number of previous x12 runs stored in the x12OldOutput slot of an x12Single-class or an x12Batch-class object that should be included in the summary.

print

TRUE/FALSE if the summary should be printed.

Methods

%\item{\code{signature(x = "ANY")}}{ %% ~~describe this method here~~ %}

signature(x = "x12Output")

signature(x = "x12Single")

signature(x = "x12Batch")

See Also

prev, cleanArchive

Examples

# NOT RUN {
# }
# NOT RUN {
# Summary of an "x12Single" object
x12path("../x12a.exe")
s <- new("x12Single",ts=AirPassengers,tsName="air")
s <- setP(s,list(estimate=TRUE,regression.variables="AO1950.1",outlier.types="all",
  outlier.critical=list(LS=3.5,TC=2.5),backcast_years=1/2))
s <- x12(s)
summary.output<-summary(s)
s <- x12(setP(s,list(arima.model=c(0,1,1),arima.smodel=c(0,2,1))))
summary.output<-summary(s,oldOutput=1)
s <- x12(setP(s,list(arima.model=c(0,1,1),arima.smodel=c(1,0,1))))
summary.output<-summary(s,fullSummary=TRUE,oldOutput=2)

# Summary of an "x12Batch" object
xb <- new("x12Batch",list(AirPassengers,AirPassengers,
				AirPassengers),tsName=c("air1","air2","air3"))
xb <- x12(xb)
xb <- setP(xb,list(arima.model=c(1,1,0),arima.smodel=c(1,1,0)),1)
xb <- x12(xb)
xb <- setP(xb,list(regression.variables=c("AO1955.5","AO1956.1","ao1959.3")),1)
xb <- setP(xb,list(regression.variables=c("AO1955.4")),2)
xb<- x12(xb)
xb <- setP(xb,list(outlier.types="all"))
xb <- setP(xb,list(outlier.critical=list(LS=3.5,TC=2.5)),1)
xb <- setP(xb,list(regression.variables=c("lpyear")),3)
xb<- x12(xb)
summary.output<-summary(xb,oldOutput=3)
# }