Version: 1.10.3
Date: 2022-05-19
Title: Interface to 'X12-ARIMA'/'X13-ARIMA-SEATS' and Structure for Batch Processing of Seasonal Adjustment
Author: Alexander Kowarik <alexander.kowarik@statistik.gv.at>, Angelika Meraner
Maintainer: Alexander Kowarik <alexander.kowarik@statistik.gv.at>
Depends: R (≥ 2.14.0),stats,utils,grDevices,x13binary
Imports: stringr,methods
Suggests: covr, parallel, tinytest
Description: The 'X13-ARIMA-SEATS' https://www.census.gov/data/software/x13as.html methodology and software is a widely used software and developed by the US Census Bureau. It can be accessed from 'R' with this package and 'X13-ARIMA-SEATS' binaries are provided by the 'R' package 'x13binary'.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
LazyData: TRUE
ByteCompile: TRUE
URL: https://github.com/statistikat/x12
NeedsCompilation: no
Packaged: 2022-05-19 07:03:27 UTC; alex
Repository: CRAN
Date/Publication: 2022-05-19 09:20:02 UTC

x12Single object

Description

x12 Single object with the AirPassengers time series

Usage

data(AirPassengersX12)

Examples

data(AirPassengersX12)
summary(AirPassengersX12)
summary(AirPassengersX12,oldOutput=10)

x12Batch object

Description

x12Batch object of four AirPassengers series with paramters and output objects

Usage

data(AirPassengersX12Batch)

Examples

data(AirPassengersX12Batch)
summary(AirPassengersX12Batch)

~~ Methods for Function crossVal in Package x12 ~~

Description

Cross Validation with function crossVal in package x12.

Usage

## S4 method for signature 'ts'
crossVal(object, x12Parameter, x12BaseInfo,
		showCI=FALSE, main="Cross Validation",
		col_original="black", col_fc="#2020ff", col_bc="#2020ff",
		col_ci="#d1d1ff", col_cishade="#d1d1ff",
		lty_original=1, lty_fc=2, lty_bc=2, lty_ci=1,
		lwd_original=1, lwd_fc=1, lwd_bc=1, lwd_ci=1, ytop=1,
		points_bc=FALSE, points_fc=FALSE, points_original=FALSE,
		showLine=TRUE, col_line="grey", lty_line=3,
		ylab="Value", xlab="Date",ylim=NULL,span=NULL)
## S4 method for signature 'x12Single'
crossVal(object, x12BaseInfo=new("x12BaseInfo"),
		showCI=FALSE, main="Cross Validation",
		col_original="black", col_fc="#2020ff", col_bc="#2020ff",
		col_ci="#d1d1ff", col_cishade="#d1d1ff",
		lty_original=1, lty_fc=2, lty_bc=2, lty_ci=1,
		lwd_original=1, lwd_fc=1, lwd_bc=1, lwd_ci=1, ytop=1,
		points_bc=FALSE, points_fc=FALSE, points_original=FALSE,
		showLine=TRUE, col_line="grey", lty_line=3,
		ylab="Value", xlab="Date",ylim=NULL,span=NULL)

Arguments

object

object of class ts or x12Single-class.

x12Parameter

object of class x12Parameter.

x12BaseInfo

object of class x12BaseInfo.

showCI

logical specifying if the prediction interval should be plotted.

main

plot title.

col_original

color of the original time series.

col_fc

color of the forecasts.

col_bc

color of the backcasts.

col_ci

color of the prediction interval.

col_cishade

color of the shading of the prediction interval.

lty_original

line type of the original time series.

lty_fc

line type of the forecasts.

lty_bc

line type of the backcasts.

lty_ci

line type of the prediction interval.

lwd_original

line width of the original time series.

lwd_fc

line width of the forecasts.

lwd_bc

line width of the backcasts.

lwd_ci

line width of the prediction interval.

ytop

multiplication factor for ylim.

points_bc

logical specifying if backcasts should additionally be indicated with points.

points_fc

logical specifying if forecasts should additionally be indicated with points.

points_original

logical specifying if the original time series should additionally be indicated with points.

showLine

logical indicating if a boundary line should be drawn before/after fore-/backcasts.

col_line

color of showLine.

lty_line

line type of showLine.

ylab

label of y-axis.

xlab

label of x-axis.

ylim

range of the y-axis

span

vector of length 4, limiting the data used for the plot.
Start and end date of said time interval can be specified by 4 integers in the format c(start year, start seasonal period, end year, end seasonal period)

Value

An S4 object of class crossValidation-class.

Methods

signature(object = "ts")
signature(object = "x12Single")

Author(s)

Alexander Kowarik, Angelika Meraner

See Also

x12, plot, plotSpec, plotSeasFac, plotRsdAcf

Examples

## Not run: 
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,forecast_years=1))

cv<-crossVal(s,showLine=TRUE)
cv

## End(Not run)

Class "crossValidation"

Description

Standardized object for saving the output of crossVal in R.

Objects from the Class

Objects can be created by calls of the form new("crossValidation", ...).

Slots

backcast:

Object of class "dfOrNULL" ~~

forecast:

Object of class "dfOrNULL" ~~

Author(s)

Alexander Kowarik, Angelika Meraner

Examples

showClass("crossValidation")

Class "diagnostics"

Description

The x12 binaries produce a file with the suffix .udg. This class is a list of a selection of its content.

Objects from the Class

Objects can be created by calls of the form new("diagnostics", ...). It is used internally by the methods for x12Batch and x12Single objects.

Slots

.Data:

Object of class "list" ~~

Extends

Class "list", from data part.

Author(s)

Alexander Kowarik

Examples

showClass("diagnostics")

Class "fbcast"

Description

Objects to save estimate, lowerci and upperci of fore- and/or backcasts in one standardized list. Used by the functions in this package.

Objects from the Class

Objects can be created by calls of the form new("fbcast", ...).

Slots

estimate:

Object of class "ts" ~~

lowerci:

Object of class "ts" ~~

upperci:

Object of class "ts" ~~

Author(s)

Alexander Kowarik

Examples

showClass("fbcast")

getP and setP for retrieving and setting parameters

Description

getP and setP for retrieving and setting parameters from a x12Single-class, x12Batch-class or x12Parameter-class object.

Usage

## S4 method for signature 'x12Single'
getP(object, whichP)
## S4 method for signature 'x12Batch'
getP(object, whichP,index=NULL)
## S4 method for signature 'x12Parameter'
getP(object, whichP)
## S4 method for signature 'x12Single'
setP(object, listP)
## S4 method for signature 'x12Batch'
setP(object, listP,index=NULL)
## S4 method for signature 'x12Parameter'
setP(object, listP)

Arguments

object

object of class x12Single-class, x12Batch-class or x12Parameter-class.

whichP

character vector with the names of the parameters to extract

listP

named list of parameters to change

index

index of the series in x12Batch-class to change or extract (NULL=all)

Methods

signature(object = "x12Batch")
signature(object = "x12Parameter")
signature(object = "x12Single")

See Also

x12, x12Single, x12Batch

Examples

## Not run: 
#Create new batch object with 4 time series
xb <- new("x12Batch",list(AirPassengers,AirPassengers,AirPassengers,AirPassengers))
# change the automdl to FALSE in all 4 elements
xb <- setP(xb,list(automdl=FALSE))
#change the arima.model and arima.smodel settings for the first ts object
xb <- setP(xb,list(arima.model=c(1,1,0),arima.smodel=c(1,1,0)),1)
#change the arima.model and arima.smodel settings for the second ts object
xb <- setP(xb,list(arima.model=c(0,1,1),arima.smodel=c(0,1,1)),2)
#change the arima.model and arima.smodel settingsfor the third ts object
xb <- setP(xb,list(arima.model=c(0,1,1),arima.smodel=c(1,1,1)),3)
#change the arima.model and arima.smodel settings for the fourth ts object
xb <- setP(xb,list(arima.model=c(1,1,1),arima.smodel=c(1,1,1)),4)
#run x12 on all series
xb <- x12(xb)
summary(xb)
#Set automdl=TRUE for the first ts
xb <- setP(xb,list(automdl=TRUE),1)
getP(xb,"automdl")
#rerun x12 on all series (the binaries will only run on the first one)
xb <- x12(xb)
#summary with oldOutput
summary(xb,oldOutput=10)
#Change the parameter and output of the first series back to the first run
xb <- prev(xb,index=1,n=1)
#summary with oldOutput (--- No valid previous runs. ---)
summary(xb,oldOutput=10)

## End(Not run)

loadP and saveP

Description

Functions loadP and saveP load and save parameter settings.

Usage

## S4 method for signature 'x12Single'
loadP(object, file)
## S4 method for signature 'x12Batch'
loadP(object, file)
## S4 method for signature 'x12Parameter'
loadP(object, file)
## S4 method for signature 'x12Single'
saveP(object, file)
## S4 method for signature 'x12Batch'
saveP(object, file)
## S4 method for signature 'x12Parameter'
saveP(object, file)

Arguments

object

object of class x12Single-class, x12Batch-class or x12Parameter-class.

file

filepath

Methods

signature(object = "x12Batch")
signature(object = "x12Parameter")
signature(object = "x12Single")

See Also

x12, x12Batch

Examples

## Not run: 
#Create new batch object with 4 time series and change some parameters
xb <- new("x12Batch",list(AirPassengers,AirPassengers,AirPassengers,AirPassengers))
xb <- setP(xb,list(automdl=FALSE))
xb <- setP(xb,list(arima.model=c(1,1,0),arima.model=c(1,1,0)),1)
xb <- setP(xb,list(arima.model=c(0,1,1),arima.smodel=c(0,1,1)),2)
xb <- setP(xb,list(arima.model=c(0,1,1),arima.smodel=c(1,1,1)),3)
xb <- setP(xb,list(arima.model=c(1,1,1),arima.smodel=c(1,1,1)),4)

#save all parameters
saveP(xb,file="xyz.RData")
xb1 <- new("x12Batch",list(AirPassengers,AirPassengers,AirPassengers,AirPassengers))

#load all parameters and save it to the corresponding series inside a x12Batch-object
xb1 <- loadP(xb1,file="xyz.RData")


xs <- new("x12Single",ts=AirPassengers)
xs <- setP(xs,list(arima.model=c(2,1,1),arima.smodel=c(2,1,1)))
#Save the parameters
saveP(xs,file="xyz1.RData")

#Load a saved parameter set to a x12Single object
xs <- new("x12Single",ts=AirPassengers)
xs <- loadP(xs,file="xyz1.RData")

#Replace all parameters in a x12Batch object with one parameter set
xb <- new("x12Batch",list(AirPassengers,AirPassengers,AirPassengers,AirPassengers))
xb <- loadP(xb,file="xyz1.RData")

## End(Not run)

~~ Methods for Function plot in Package x12 ~~

Description

Plot function for x12 output in package x12.

Usage

## S4 method for signature 'x12Single'
plot(x, original=TRUE, sa=FALSE, trend=FALSE, log_transform=FALSE,
  ylab="Value", xlab="Date", main="TS", col_original="black", col_sa="blue",
  col_trend="green", lwd_original=1, lwd_sa=1, lwd_trend=1, lty_sa=1, lty_trend=1, ytop=1,
  showAllout=FALSE, showAlloutLines=FALSE, showOut=NULL, annComp=TRUE, annCompTrend=TRUE,
  col_ao="red", col_ls="red", col_tc="red", col_annComp="grey", lwd_out=1, cex_out=1.5,
  pch_ao=4, pch_ls=2, pch_tc=23, plot_legend=TRUE, legend_horiz=TRUE, legend_bty="o",
  forecast=FALSE, backcast=FALSE,
  showCI=TRUE, col_fc="#2020ff", col_bc="#2020ff", col_ci="#d1d1ff",
  col_cishade="#d1d1ff", lty_original=1, lty_fc=2, lty_bc=2, lty_ci=1, lwd_fc=1, lwd_bc=1,
  lwd_ci=1, points_bc=FALSE, points_fc=FALSE, points_original=FALSE, showLine=FALSE,
  col_line="grey", lty_line=3, ylim=NULL, span=NULL, ...)
## S4 method for signature 'x12Batch'
plot(x, what="ask",original=TRUE, sa=FALSE, trend=FALSE, log_transform=FALSE,
  ylab="Value", xlab="Date", main="TS", col_original="black", col_sa="blue",
  col_trend="green", lwd_original=1, lwd_sa=1, lwd_trend=1, lty_sa=1, lty_trend=1, ytop=1,
  showAllout=FALSE, showAlloutLines=FALSE, showOut=NULL, annComp=TRUE, annCompTrend=TRUE,
  col_ao="red", col_ls="red", col_tc="red", col_annComp="grey", lwd_out=1, cex_out=1.5,
  pch_ao=4, pch_ls=2, pch_tc=23, plot_legend=TRUE, legend_horiz=TRUE, legend_bty="o",
  forecast=FALSE, backcast=FALSE,
  showCI=TRUE, col_fc="#2020ff", col_bc="#2020ff", col_ci="#d1d1ff",
  col_cishade="#d1d1ff", lty_original=1, lty_fc=2, lty_bc=2, lty_ci=1, lwd_fc=1, lwd_bc=1,
  lwd_ci=1, points_bc=FALSE, points_fc=FALSE, points_original=FALSE, showLine=FALSE,
  col_line="grey", lty_line=3, ylim=NULL, span=NULL, ...)
## S4 method for signature 'x12Output'
plot(x, original=TRUE, sa=FALSE, trend=FALSE, log_transform=FALSE,
  ylab="Value", xlab="Date", main="TS", col_original="black", col_sa="blue",
  col_trend="green", lwd_original=1, lwd_sa=1, lwd_trend=1, lty_sa=1, lty_trend=1, ytop=1,
  showAllout=FALSE, showAlloutLines=FALSE, showOut=NULL, annComp=TRUE, annCompTrend=TRUE,
  col_ao="red", col_ls="red", col_tc="red", col_annComp="grey", lwd_out=1, cex_out=1.5,
  pch_ao=4, pch_ls=2, pch_tc=23, plot_legend=TRUE, legend_horiz=TRUE, legend_bty="o",
  forecast=FALSE, backcast=FALSE, showCI=TRUE,
  col_fc="#2020ff", col_bc="#2020ff", col_ci="#d1d1ff", col_cishade="#d1d1ff",
  lty_original=1, lty_fc=2, lty_bc=2, lty_ci=1, lwd_fc=1, lwd_bc=1, lwd_ci=1,
  points_bc=FALSE, points_fc=FALSE, points_original=FALSE,
  showLine=FALSE, col_line="grey", lty_line=3, ylim=NULL, span=NULL, ...)

Arguments

x

object of class x12Output-class or x12Single-class.

original

logical defining whether the original time series should be plotted.

sa

logical defining whether the seasonally adjusted time series should be plotted.

trend

logical defining whether the trend should be plotted.

log_transform

logical defining whether the log transform should be plotted.

showAllout

logical defining whether all outliers should be plotted.

showOut

character in the format "TypeYear.Seasonalperiod" defining a specific outlier to be plotted.

annComp

logical defining whether an annual comparison should be performed for the outlier defined in showOut.

forecast

logical defining whether the forecasts should be plotted.

backcast

logical defining whether the backcasts should be plotted.

showCI

logical defining whether the prediction intervals should be plotted.

ylab

label of y-axis.

xlab

label of x-axis.

main

plot title.

col_original

color of the original time series.

col_sa

color of the seasonally adjusted time series.

col_trend

color of the trend.

lwd_original

line width of the original time series.

lwd_sa

line width of the seasonally adjusted time series.

lwd_trend

line width of the trend.

lty_original

line type of the original time series.

lty_sa

line type of the seasonally adjusted time series.

lty_trend

line type of the trend.

ytop

multiplication factor for ylim.

showAlloutLines

logical specifying if vertical lines should be plotted with the outliers.

annCompTrend

logical specifying if the trend of the annual comparison should be plotted.

col_ao

color of additive outliers.

col_ls

color of level shifts.

col_tc

color of transitory changes.

col_annComp

color of annual comparison.

lwd_out

line width of outliers.

cex_out

magnification factor for size of symbols used for plotting outliers.

pch_ao

symbols used for additive outliers.

pch_ls

symbols used for level shifts.

pch_tc

symbols used for transitory changes.

plot_legend

logical specifying if a legend should be plotted.

legend_horiz

Orientation of the legend

legend_bty

the type of box to be drawn around the legend. The allowed values are "o" (the default) and "n".

col_fc

color of forecasts.

col_bc

color of backcasts.

col_ci

color of prediction interval.

col_cishade

color of prediction interval shading.

lty_fc

line type of forecasts.

lty_bc

line type of backcasts.

lty_ci

line type of prediction interval.

lwd_fc

line width of forecasts.

lwd_bc

line width of backcasts.

lwd_ci

line width of prediction interval.

points_bc

logical specifying if backcasts should additionally be indicated with points.

points_fc

logical specifying if forecasts should additionally be indicated with points.

points_original

logical specifying if the original time series should additionally be indicated with points.

showLine

logical indicating if a boundary line should be drawn before/after fore-/backcasts.

col_line

color of showLine.

lty_line

line type of showLine.

ylim

range of the y-axis.

span

vector of length 4, limiting the data used for the plot.
Start and end date of said time interval can be specified by 4 integers in the format c(start year, start seasonal period, end year, end seasonal period)

what

How multiple plots should be treated. "ask" is the only option at the moment.

...

ignored.

Methods

signature(x = "x12Output")
signature(x = "x12Single")

Author(s)

Alexander Kowarik, Angelika Meraner

See Also

plotSpec, plotSeasFac, plotRsdAcf

Examples

## Not run: 
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)
#w/o outliers
plot(s@x12Output,sa=TRUE,trend=TRUE,original=FALSE)
plot(s)
#with (all) outliers
plot(s,showAllout=TRUE,sa=TRUE,trend=TRUE,log_transform=TRUE,lwd_out=1,pch_ao=4)
plot(s,showAllout=TRUE,sa=TRUE,trend=TRUE,original=FALSE,showAlloutLines=TRUE,
  col_tc="purple")#,log_transform=TRUE)#,lwd_out=3)
plot(s,showAllout=TRUE,span=c(1951,1,1953,12),points_original=TRUE,cex_out=2)
#with showOut
plot(s,showOut="AO1960.Jun",sa=FALSE,trend=FALSE,annComp=TRUE,log_transform=TRUE)
plot(s,showOut="AO1958.Mar",sa=TRUE,trend=TRUE,annComp=TRUE,annCompTrend=FALSE)
plot(s,showOut="AO1950.Jun",annComp=FALSE,cex_out=3,pch_ao=19,col_ao="orange")
plot(s,showOut="TC1954.Mar",span=c(1954,1,1955,12))
plot(s,showOut="TC1954.Feb",col_tc="green3")

#w/o legend
plot(s,showAllout=TRUE,plot_legend=FALSE)
plot(s,plot_legend=FALSE)
plot(s,showOut="AO1950.1",plot_legend=FALSE,lwd_out=2,col_ao="purple")
plot(s,showOut="TC1954.Feb",col_tc="orange",col_ao="magenta",plot_legend=FALSE)
plot(s,showOut="AO1950.1",col_tc="orange",col_ao="magenta",plot_legend=FALSE)

#Forecasts & Backcasts
plot(s,forecast=TRUE)
plot(s,backcast=TRUE,showLine=TRUE)
plot(s,backcast=TRUE,forecast=TRUE,showCI=FALSE)
plot(s,forecast=TRUE,points_fc=TRUE,col_fc="purple",lty_fc=2,lty_original=3,
  lwd_fc=0.9,lwd_ci=2)
plot(s,sa=TRUE,plot_legend=FALSE)

#Seasonal Factors and SI Ratios
plotSeasFac(s)
#Spectra
plotSpec(s)
plotSpec(s,highlight=FALSE)
#Autocorrelations of the Residuals
plotRsdAcf(s)
plotRsdAcf(s,col_acf="black",lwd_acf=1)

## End(Not run)

Plot method for objects of class x12work

Description

Plot method for objects of class "x12work".

Usage

## S3 method for class 'x12work'
plot(x,plots=c(1:9), ...)

Arguments

x

an object of class "x12work".

plots

a vector containing numbers between 1 and 9.

...

further arguments (currently ignored).

Details

Plots:
1: Original
2: Original Trend Adjusted
3: Log Original
4: Seasonal Factors
5: Seasonal Factors with SI Ratios
6: Spectrum Adjusted Original
7: Spectrum Seasonal Adjusted
8: Spectrum Irregular
9: Spectrum Residulas

Author(s)

Alexander Kowarik

See Also

x12work

Examples

  data(AirPassengersX12)
  #plot(AirPassengersX12)

~~ Methods for Function plotRsdAcf in Package x12 ~~

Description

Plot of the (partial) autocorrelations of the (squared) residuals with function plotRsdAcf in package x12.

Usage

## S4 method for signature 'x12Output'
plotRsdAcf(x, which="acf",
				xlab="Lag", ylab="ACF",
				main="default", col_acf="darkgrey", lwd_acf=4,
				col_ci="blue", lt_ci=2, ylim="default", ...)
## S4 method for signature 'x12Single'
plotRsdAcf(x, which="acf",
				xlab="Lag", ylab="ACF",
				main="default", col_acf="darkgrey", lwd_acf=4,
				col_ci="blue", lt_ci=2, ylim="default", ...)

Arguments

x

object of class x12Output-class or x12Single-class.

which

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

xlab

label of the x-axis.

ylab

label of the y-axis.

main

plot title.

col_acf

color of the autocorrelations.

lwd_acf

line width of the autocorrelations.

col_ci

color of the +- 2 standard error limits.

lt_ci

line type of the +- 2 standard error limits.

ylim

range of the y-axis.

...

ignored.

Methods

signature(x = "x12Output")
signature(x = "x12Single")

Author(s)

Alexander Kowarik, Angelika Meraner

See Also

x12, plot, plotSpec, plotSeasFac

Examples

## Not run: 
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)
#w/o outliers
plot(s@x12Output,sa=TRUE,trend=TRUE,original=FALSE)
plot(s)
#with (all) outliers
plot(s,showAllout=TRUE,sa=TRUE,trend=TRUE,log_transform=TRUE,lwd_out=1,pch_ao=4)
plot(s,showAllout=TRUE,sa=TRUE,trend=TRUE,original=FALSE,showAlloutLines=TRUE,
  col_tc="purple")#,log_transform=TRUE)#,lwd_out=3)
#with showOut
plot(s,showOut="AO1960.Jun",sa=FALSE,trend=FALSE,annComp=TRUE,log_transform=TRUE)
plot(s,showOut="AO1958.Mar",sa=TRUE,trend=TRUE,annComp=TRUE,annCompTrend=FALSE)
plot(s,showOut="AO1950.Jun",annComp=FALSE,cex_out=3,pch_ao=19,col_ao="orange")
plot(s,showOut="TC1954.Feb")
plot(s,showOut="TC1954.Feb",col_tc="green3")

#w/o legend
plot(s,showAllout=TRUE,plot_legend=FALSE)
plot(s,plot_legend=FALSE)
plot(s,showOut="AO1950.1",plot_legend=FALSE,lwd_out=2,col_ao="purple")
plot(s,showOut="TC1954.Feb",col_tc="orange",col_ao="magenta",plot_legend=FALSE)
plot(s,showOut="AO1950.1",col_tc="orange",col_ao="magenta",plot_legend=FALSE)

#Forecasts & Backcasts
plot(s,forecast=TRUE)
plot(s,backcast=TRUE,showLine=TRUE)
plot(s,backcast=TRUE,forecast=TRUE,showCI=FALSE)
plot(s,forecast=TRUE,points_fc=TRUE,col_fc="purple",lty_fc=2,lty_original=3,lwd_fc=0.9,
  lwd_ci=2)
plot(s,sa=TRUE,plot_legend=FALSE)

#Seasonal Factors and SI Ratios
plotSeasFac(s)
#Spectra
plotSpec(s)
plotSpec(s,highlight=FALSE)
#Autocorrelations of the Residuals
plotRsdAcf(s)
plotRsdAcf(s,col_acf="black",lwd_acf=1)

## End(Not run)

~~ Methods for Function plotSeasFac in Package x12 ~~

Description

Seasonal factor plots with function plotSeasFac in package x12.

Usage

## S4 method for signature 'x12Output'
plotSeasFac(x,SI_Ratios=TRUE, ylab="Value", xlab="",
  lwd_seasonal=1, col_seasonal="black", lwd_mean=1, col_mean="blue",
  col_siratio="darkgreen",col_replaced="red", cex_siratio=.9, cex_replaced=.9,
  SI_Ratios_replaced=TRUE, plot_legend=TRUE,legend_horiz=FALSE,legend_bty="o",
   ...)
## S4 method for signature 'x12Single'
plotSeasFac(x,SI_Ratios=TRUE, ylab="Value", xlab="",lwd_seasonal=1,
  col_seasonal="black", lwd_mean=1, col_mean="blue", col_siratio="darkgreen",
  col_replaced="red", cex_siratio=.9, cex_replaced=.9, SI_Ratios_replaced=TRUE,
  plot_legend=TRUE,legend_horiz=FALSE,legend_bty="o",
  ...)

Arguments

x

object of class x12Output-class or x12Single-class.

SI_Ratios

logical specifying if the SI ratios should be plotted.

ylab

label of the y-axis.

xlab

label of the x-axis.

lwd_seasonal

line width of the seasonal factors.

col_seasonal

color of the seasonal factors.

lwd_mean

line width of the mean.

col_mean

color of the mean.

col_siratio

color of the SI ratios.

col_replaced

color of the replaced SI ratios.

cex_siratio

magnification factor for the size of the symbols used for plotting the SI ratios.

cex_replaced

magnification factor for the size of the symbols used for plotting the replaced SI ratios.

SI_Ratios_replaced

logical specifying if the replaced SI ratios should be plotted.

plot_legend

logical specifying if a legend should be plotted.

legend_horiz

Orientation of the legend

legend_bty

the type of box to be drawn around the legend. The allowed values are "o" (the default) and "n".

...

ignored.

Methods

signature(x = "x12Output")
signature(x = "x12Single")

Author(s)

Alexander Kowarik, Angelika Meraner

See Also

x12, plot, plotSpec, plotRsdAcf

Examples


## Not run: 
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)
#w/o outliers
plot(s@x12Output,sa=TRUE,trend=TRUE,original=FALSE)
plot(s)
#with (all) outliers
plot(s,showAllout=TRUE,sa=TRUE,trend=TRUE,log_transform=TRUE,lwd_out=1,pch_ao=4)
plot(s,showAllout=TRUE,sa=TRUE,trend=TRUE,original=FALSE,showAlloutLines=TRUE,
  col_tc="purple")#,log_transform=TRUE)#,lwd_out=3)
#with showOut
plot(s,showOut="AO1960.Jun",sa=FALSE,trend=FALSE,annComp=TRUE,log_transform=TRUE)
plot(s,showOut="AO1958.Mar",sa=TRUE,trend=TRUE,annComp=TRUE,annCompTrend=FALSE)
plot(s,showOut="AO1950.Jun",annComp=FALSE,cex_out=3,pch_ao=19,col_ao="orange")
plot(s,showOut="TC1954.Feb")
plot(s,showOut="TC1954.Feb",col_tc="green3")

#w/o legend
plot(s,showAllout=TRUE,plot_legend=FALSE)
plot(s,plot_legend=FALSE)
plot(s,showOut="AO1950.1",plot_legend=FALSE,lwd_out=2,col_ao="purple")
plot(s,showOut="TC1954.Feb",col_tc="orange",col_ao="magenta",plot_legend=FALSE)
plot(s,showOut="AO1950.1",col_tc="orange",col_ao="magenta",plot_legend=FALSE)

#Forecasts & Backcasts
plot(s,forecast=TRUE)
plot(s,backcast=TRUE,showLine=TRUE)
plot(s,backcast=TRUE,forecast=TRUE,showCI=FALSE)
plot(s,forecast=TRUE,points_fc=TRUE,col_fc="purple",lty_fc=2,lty_original=3,
  lwd_fc=0.9,lwd_ci=2)
plot(s,sa=TRUE,plot_legend=FALSE)

#Seasonal Factors and SI Ratios
plotSeasFac(s)
#Spectra
plotSpec(s)
plotSpec(s,highlight=FALSE)
#Autocorrelations of the Residuals
plotRsdAcf(s)
plotRsdAcf(s,col_acf="black",lwd_acf=1)

## End(Not run)

~~ Methods for Function plotSpec in Package x12 ~~

Description

Spectral plots with function plotSpec in package x12.

Arguments

x

an object of class x12Output-class, x12Single-class or spectrum-class.

which

a string defining the executable of the editor to use ("sa" for the Spectrum of the Seasonally Adjusted Series, "original" for the Spectrum of the Original Series, "irregular" for the Spectrum of the Irregular Series and "residuals" for the Spectrum of the RegARIMA Residuals).

frequency

frequency of the time series (has to be specified for objects of class "spectrum" only).

xlab

label of the x-axis.

ylab

label of the y-axis.

main

plot title.

col_bar

color of bars.

col_seasonal

color of seasonal frequencies.

col_td

color of trading day frequencies.

lwd_bar

line width of bars.

lwd_seasonal

line width of seasonal frequencies.

lwd_td

line width of trading day frequencies.

plot_legend

logical specifying if a legend should be plotted.

Methods

signature(x = "x12Output",which="sa", xlab="Frequency",ylab="Decibels", main="Spectrum", col_bar="darkgrey",col_seasonal="red",col_td="blue", lwd_bar=4,lwd_seasonal=4,lwd_td=4,plot_legend=TRUE,...)
signature(x = "x12Single",which="sa", xlab="Frequency",ylab="Decibels", main="Spectrum", col_bar="darkgrey",col_seasonal="red",col_td="blue", lwd_bar=4,lwd_seasonal=4,lwd_td=4,plot_legend=TRUE,...)
signature(x = "spectrum",frequency, xlab="Frequency",ylab="Decibels", main="Spectrum", col_bar="darkgrey",col_seasonal="red",col_td="blue", lwd_bar=4,lwd_seasonal=4,lwd_td=4,plot_legend=TRUE,...)

Author(s)

Alexander Kowarik, Angelika Meraner

See Also

x12, plot, plotSeasFac, plotRsdAcf

Examples


## Not run: 
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)
#w/o outliers
plot(s@x12Output,sa=TRUE,trend=TRUE,original=FALSE)
plot(s)
#with (all) outliers
plot(s,showAllout=TRUE,sa=TRUE,trend=TRUE,log_transform=TRUE,lwd_out=1,pch_ao=4)
plot(s,showAllout=TRUE,sa=TRUE,trend=TRUE,original=FALSE,showAlloutLines=TRUE,
  col_tc="purple")#,log_transform=TRUE)#,lwd_out=3)
#with showOut
plot(s,showOut="AO1960.Jun",sa=FALSE,trend=FALSE,annComp=TRUE,log_transform=TRUE)
plot(s,showOut="AO1958.Mar",sa=TRUE,trend=TRUE,annComp=TRUE,annCompTrend=FALSE)
plot(s,showOut="AO1950.Jun",annComp=FALSE,cex_out=3,pch_ao=19,col_ao="orange")
plot(s,showOut="TC1954.Feb")
plot(s,showOut="TC1954.Feb",col_tc="green3")

#w/o legend
plot(s,showAllout=TRUE,plot_legend=FALSE)
plot(s,plot_legend=FALSE)
plot(s,showOut="AO1950.1",plot_legend=FALSE,lwd_out=2,col_ao="purple")
plot(s,showOut="TC1954.Feb",col_tc="orange",col_ao="magenta",plot_legend=FALSE)
plot(s,showOut="AO1950.1",col_tc="orange",col_ao="magenta",plot_legend=FALSE)

#Forecasts & Backcasts
plot(s,forecast=TRUE)
plot(s,backcast=TRUE,showLine=TRUE)
plot(s,backcast=TRUE,forecast=TRUE,showCI=FALSE)
plot(s,forecast=TRUE,points_fc=TRUE,col_fc="purple",lty_fc=2,lty_original=3,
  lwd_fc=0.9,lwd_ci=2)
plot(s,sa=TRUE,plot_legend=FALSE)

#Seasonal Factors and SI Ratios
plotSeasFac(s)
#Spectra
plotSpec(s)
plotSpec(s,highlight=FALSE)
#Autocorrelations of the Residuals
plotRsdAcf(s)
plotRsdAcf(s,col_acf="black",lwd_acf=1)

## End(Not run)

~~ Methods for Function prev and cleanArchive in Package x12 ~~

Description

Function prev in package x12 reverts to previous parameter settings and output.
Function cleanHistory resets x12OldParameter and x12OldOutput.

Usage

## S4 method for signature 'x12Single'
prev(object,n=NULL)
## S4 method for signature 'x12Batch'
prev(object,index=NULL,n=NULL)
## S4 method for signature 'x12Single'
cleanHistory(object)
## S4 method for signature 'x12Batch'
cleanHistory(object,index=NULL)

Arguments

object

object of class x12Single-class or x12Batch-class.

n

index corresponding to a previous run.

index

index corresponding to (an) object(s) of class "x12Single".

Methods

signature(object = "x12Single")
signature(object = "x12Batch")

Note

cleanHistory is deprecated and cleanArchive should be used instead.

Author(s)

Alexander Kowarik

See Also

x12

Examples

data(AirPassengersX12)
summary(AirPassengersX12)
# a maximum of 10 previous x12 runs are added to the summary
summary(AirPassengersX12,oldOutput=10)
#the x12Parameter and x12Output of the x12Single is set to the previous run of x12
Ap=prev(AirPassengersX12)
summary(AirPassengersX12,oldOutput=10)

Function to read X12-spc Files in x12Parameter R objects

Description

Still an early beta, so it will not work in specific situations

Usage

readSpc(file,filename=TRUE)

Arguments

file

character vector containing filenames of spc files

filename

if TRUE the filename (without) ".spc" will be used as name for the serie

Details

Not all arguments of an X12 spc file are supported, but the parameters described in x12 should be covered.

Value

The function returns an object of class "x12Single" if the file argument has length 1, otherwise it returns an "x12Batch" object.

Author(s)

Alexander Kowarik

See Also

x12

Examples

## Not run: 
  x12SingleObject1 <- readSpc("D:/aaa.spc")
  x12SingleObject2 <- readSpc("D:/ak_b.SPC")
  x12BatchObject1 <- readSpc(c("D:/ak_b.SPC","D:/aaa.spc"))
  setwd("M:/kowarik/Test/x12test")
  lf <- list.files()
  lf <- lf[unlist(lapply(lf,function(x)substr(x,nchar(x)-2,nchar(x)))) %in%c("spc","SPC")]
  lf <- lf[-c(grep("ind",lf))]
  allSPC <- readSpc(lf)
  a <- x12(allSPC)  
  plot(a@x12List[[1]])
  summary(a@x12List[[1]])

## End(Not run)

Class "spectrum"

Description

Standardized object for saving the spectrum output of the x12 binaries in R. Used by functions in this package.

Objects from the Class

Objects can be created by calls of the form new("spectrum", ...).

Slots

frequency:

Object of class "numeric" ~~

spectrum:

Object of class "numeric" ~~

Author(s)

Alexander Kowarik

Examples

showClass("spectrum")

~~ Methods for Function summary in Package x12 ~~

Description

Delivers a diagnostics summary for x12 output.

Usage

## S4 method for signature '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 signature '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 signature '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

signature(x = "x12Output")
signature(x = "x12Single")
signature(x = "x12Batch")

Author(s)

Alexander Kowarik, Angelika Meraner

See Also

prev, cleanArchive

Examples


## 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)

## End(Not run)

Diagnostics summary for objects of class x12work

Description

Diagnostics summary for objects of class "x12work".

Usage

## S3 method for class 'x12work'
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,...)

Arguments

object

an object of class "x12work".

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.

...

ignored at the moment

Details

Delivers a diagnostics summary.

Author(s)

Alexander Kowarik, Angelika Meraner

See Also

x12work, diagnostics-class, x12-methods

Examples

  data(AirPassengers)
## Not run: 
summary(x12work(AirPassengers,...),quality.stat=TRUE,res.autocorr="acf")
## End(Not run)

Read start and end of a x12Single or x12Output object

Description

Combination of start() and end() for ts objects-

Usage

  times(x)
  ## S4 method for signature 'x12Output'
times(x)
  ## S4 method for signature 'x12Single'
times(x)

Arguments

x

a x12Single or x12Output object

Value

Returns a list with start and end for original, backcast and forecast timeseries

Methods

signature(x = "x12Output")
signature(x = "x12Single")

Author(s)

Alexander Kowarik

See Also

x12, x12Single, x12Batch, x12Parameter, x12List, x12Output, x12BaseInfo, summary.x12work, x12work

~~ Methods for Function x12 in Package x12 ~~

Description

~~ Methods for function x12 in package x12 ~~

Usage

x12(object,x12Parameter=new("x12Parameter"),x12BaseInfo=new("x12BaseInfo"),...)

Arguments

object

object of class ts, x12Single-class or x12Batch-class

x12Parameter

object of class x12Parameter

x12BaseInfo

object of class x12BaseInfo

...

at the moment only forceRun=FALSE

Methods

signature(object = "ts")
signature(object = "x12Single")
signature(object = "x12Batch")

Value

x12Output

An S4 object of class x12Output-class if object is of class ts

x12Single

An S4 object of class x12Single-class if object is of class x12Single-class

x12Batch

An S4 object of class x12Batch-class if object is of class x12Batch-class

Note

Parallelization is implemented for x12Batch objects with help of the package 'parallel'. To process in parallel set the option 'x12.parallel' to an integer value representing the number of cores to use ( options(x12.parallel=2) ). Afterwards all calls to the function 'x12' on an object of class 'x12Batch' will be parallelized (For reseting use options(x12.parallel=NULL) ).

cleanHistory is deprecated and cleanArchive should be used instead.

Author(s)

Alexander Kowarik, Angelika Meraner

Source

https://www.census.gov/data/software/x13as.html

References

Alexander Kowarik, Angelika Meraner, Matthias Templ, Daniel Schopfhauser (2014). Seasonal Adjustment with the R Packages x12 and x12GUI. Journal of Statistical Software, 62(2), 1-21. URL http://www.jstatsoft.org/v62/i02/.

See Also

summary, plot, x12env, setP, getP, loadP, saveP, prev, cleanArchive, crossVal

Examples

## Not run: 
xts <- x12(AirPassengers)
summary(xts)
xs <- x12(new("x12Single",ts=AirPassengers))
summary(xs)


xb<-x12(new("x12Batch",list(AirPassengers,AirPassengers,AirPassengers)))
summary(xb)

#Create new batch object with 4 time series
xb <- new("x12Batch",list(AirPassengers,AirPassengers,AirPassengers,AirPassengers))

# change the automdl to FALSE in all 4 elements
xb <- setP(xb,list(automdl=FALSE))
#change the arima.model and arima.smodel setting for the first ts object
xb <- setP(xb,list(arima.model=c(1,1,0),arima.smodel=c(1,1,0)),1)
#change the arima.model and arima.smodel setting for the second ts object
xb <- setP(xb,list(arima.model=c(0,1,1),arima.smodel=c(0,1,1)),2)
#change the arima.model and arima.smodel setting for the third ts object
xb <- setP(xb,list(arima.model=c(0,1,1),arima.smodel=c(1,1,1)),3)
#change the arima.model and arima.smodel setting for the fourth ts object
xb <- setP(xb,list(arima.model=c(1,1,1),arima.smodel=c(1,1,1)),4)
#run x12 on all series
xb <- x12(xb)
summary(xb)

#Set automdl=TRUE for the first ts
xb <- setP(xb,list(automdl=TRUE),1)

#rerun x12 on all series (the binaries will only run on the first one)
xb <- x12(xb)

#summary with oldOutput
summary(xb,oldOutput=10)

#Change the parameter and output of the first series back to the first run
xb <- prev(xb,index=1,n=1)

#summary with oldOutput (--- No valid previous runs. ---)
summary(xb,oldOutput=10)

## End(Not run)

Class "x12BaseInfo"

Description

Baseinfo for use with the x12 function and classes.

Objects from the Class

Objects can be created by calls of the form new("x12BaseInfo", x12path, use, showWarnings).

Slots

x12path:

Object of class "characterOrNULL" ~~

use:

Object of class "character" ~~

showWarnings:

Object of class "logical" ~~

Methods

No methods defined with class "x12BaseInfo" in the signature.

Author(s)

Alexander Kowarik

See Also

x12, x12Single, x12Batch, x12Parameter, x12List, x12Output

Examples

showClass("x12BaseInfo")

Class "x12Batch"

Description

Concatenation of multiple x12Single-class objects.

Objects from the Class

Objects can be created by calls of the form new("x12Batch", tsList, tsName, x12BaseInfo).

Slots

x12List:

Object of class "x12List" ~~

x12BaseInfo:

Object of class "x12BaseInfo" ~~

Methods

setP

signature(object = "x12Batch"): ...

getP

signature(object = "x12Batch"): ...

prev

signature(object = "x12Batch"): ...

cleanArchive

signature(object = "x12Batch"): ...

loadP

signature(object = "x12Batch"): ...

saveP

signature(object = "x12Batch"): ...

summary

signature(object = "x12Batch"): ...

x12

signature(object = "x12Batch"): ...

dim

signature(x = "x12Batch"): ...

length

signature(x = "x12Batch"): ...

cleanHistory

signature(object = "x12Batch"): ...

Note

cleanHistory is deprecated and cleanArchive should be used instead.

Author(s)

Alexander Kowarik

References

Alexander Kowarik, Angelika Meraner, Matthias Templ, Daniel Schopfhauser (2014). Seasonal Adjustment with the R Packages x12 and x12GUI. Journal of Statistical Software, 62(2), 1-21. URL http://www.jstatsoft.org/v62/i02/.

See Also

x12, x12Single, x12Parameter, x12List, x12Output, x12BaseInfo, summary, getP, x12work

Examples

## Not run: 
#object containing 4 time series and the corresponding parameters and output
data(AirPassengersX12Batch)
summary(AirPassengersX12Batch)
#summary with oldOutput
summary(AirPassengersX12Batch,oldOutput=10)
#Change the parameter and output of the first series back to the first run
AirPassengersX12Batch <- prev(AirPassengersX12Batch,index=1,n=1)
#summary with oldOutput (--- No valid previous runs. ---)
summary(AirPassengersX12Batch,oldOutput=10)

#Create new batch object with 4 time series
xb <- new("x12Batch",list(AirPassengers,ldeaths,nottem,UKgas),
  c("Air","ldeaths","nottem","UKgas"))
# change outlier.types to "all" in all 4 elements
xb <- setP(xb,list(outlier.types="all"))
#change the arima.model and arima.smodel setting for the first ts object
xb <- setP(xb,list(arima.model=c(0,1,1),arima.smodel=c(0,1,1)),1)
#change the arima.model and arima.smodel setting for the second ts object
xb <- setP(xb,list(arima.model=c(0,1,1),arima.smodel=c(0,1,1)),2)
#change the arima.model and arima.smodel setting for the third ts object
xb <- setP(xb,list(arima.model=c(0,1,1),arima.smodel=c(0,1,1)),3)
#change the arima.model and arima.smodel setting for the fourth ts object
xb <- setP(xb,list(arima.model=c(0,1,1),arima.smodel=c(0,1,1)),4)
#run x12 on all series
xb <- x12(xb)
summary(xb)
#Set automdl=TRUE for the first ts
xb <- setP(xb,list(automdl=TRUE),1)
#rerun x12 on all series (the binaries will only run on the first one)
xb <- x12(xb)
#summary with oldOutput
summary(xb,oldOutput=10)
#Change the parameter and output of the first series back to the first run
xb <- prev(xb,index=1,n=1)
#summary with oldOutput (--- No valid previous runs. ---)
summary(xb,oldOutput=10)

#Create new batch object by combining objects of class x12Single
s1 <- new("x12Single",ts=AirPassengers,tsName="air")
s1 <- setP(s1,list(estimate=TRUE,regression.variables="AO1950.1",outlier.types="all",
  outlier.critical=list(LS=3.5,TC=2.5)))
s2 <- new("x12Single",ts=UKgas,tsName="UKgas")
s2 <- setP(s2,list(slidingspans=TRUE,history=TRUE,
  history.estimates=c("sadj","sadjchng","trend","trendchng","seasonal","aic"),
  history.sadjlags=c(1,2),automdl=TRUE))
b <- new("x12Batch",list(s1,s2))
b <- x12(b)

## End(Not run)

Class "x12List"

Description

Support class for x12Batch-class containing multiple x12Single-class.

Objects from the Class

Objects can be created by calls of the form new("x12List", ...).

Slots

.Data:

Object of class "list" ~~

Extends

Class "list", from data part. Class "vector", by class "list", distance 2.

Methods

No methods defined with class "x12List" in the signature.

Author(s)

Alexander Kowarik

See Also

x12, x12Single, x12Batch, x12Parameter, x12Output, x12BaseInfo

Examples

showClass("x12List")

Class "x12Output"

Description

Output class for x12.

Objects from the Class

Objects can be created by calls of the form new("x12Output", ...).

Slots

a1:

Object of class "ts" - the original time series.

d10:

Object of class "ts" - the final seasonal factors.

d11:

Object of class "ts" - the final seasonally adjusted data.

d12:

Object of class "ts" - the final trend cycle.

d13:

Object of class "ts" - the final irregular components.

d16:

Object of class "ts" - the combined adjustment factors.

c17:

Object of class "ts" - the final weights for the irregular component.

d9:

Object of class "ts" - the final replacements for the SI ratios.

e2:

Object of class "ts" - the differenced, transformed, seasonally adjusted data.

d8:

Object of class "ts" - the final unmodified SI ratios.

b1:

Object of class "ts" - the prior adjusted original series.

td:

Object of class "tsOrNULL" - the trading day component

otl:

Object of class "tsOrNULL" - the outlier regression series

sp0:

Object of class "spectrum" - the spectrum of the original series.

sp1:

Object of class "spectrum" - the spectrum of the differenced seasonally adjusted series.

sp2:

Object of class "spectrum" - the spectrum of modified irregular series.

spr:

Object of class "spectrum" - the spectrum of the regARIMA model residuals.

forecast:

Object of class "fbcast" - the point forecasts with prediction intervals

backcast:

Object of class "fbcast" - the point backcasts with prediction intervals

dg:

Object of class "list", containing several seasonal adjustment and regARIMA modeling diagnostics, i.e.:
x11regress, transform, samode, seasonalma, trendma, arimamdl, automdl, regmdl, nout, nautoout, nalmostout, almostoutlier, crit, outlier, userdefined, autooutlier, peaks.seas, peaks.td, id.seas, id.rsdseas, spcrsd, spcori, spcsa, spcirr, m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, m11, q, q2, nmfail, loglikelihood, aic, aicc, bic, hq, aape, autotransform, ifout, rsd.acf, rsd.pacf, rsd.acf2, tsName, frequency, span,...

file:

Object of class "character" - path to the output directory and filename

tblnames:

Object of class "character" - tables read into R

Rtblnames:

Object of class "character" - names of tables read into R

Methods

summary

signature(object = "x12Output"): ...

plot

signature(object = "x12Output"): ...

plotSpec

signature(object = "x12Output"): ...

plotSeasFac

signature(object = "x12Output"): ...

plotRsdAcf

signature(object = "x12Output"): ...

Author(s)

Alexander Kowarik, Angelika Meraner

See Also

x12, x12Single, x12Batch, x12Parameter, x12List, x12Output, x12BaseInfo, summary.x12work, x12work

Examples

data(AirPassengersX12)
summary(AirPassengersX12)
showClass("x12Output")

Class "x12Parameter"

Description

Parameter class for x12.

Objects from the Class

Objects can be created by calls of the form new("x12Parameter", ...).

Slots

series.span:

Object of class "numericOrNULLOrcharacter" - vector of length 4, limiting the data used for the calculations and analysis to a certain time interval.
Start and end date of said time interval can be specified by 4 integers in the format c(start year, start seasonal period, end year, end seasonal period) If the start or end date of the time series object should be used, the respective year and seasonal period are to be set to NA.

series.modelspan:

Object of class "numericOrNULLOrcharacter" - vector of length 4, defining the start and end date of the time interval of the data that should be used to determine all regARIMA model coefficients. Specified in the same way as span.

transform.function:

Object of class "character" - transform parameter for x12 ("auto", "log", "none").

transform.power:

Object of class "numericOrNULL" - numeric value specifying the power of the Box Cox power transformation.

transform.adjust:

Object of class "characterOrNULL" - determines the type of adjustment to be performed, i.e. transform.adjust="lom" for length-of-month adjustment on monthly data, transform.adjust="loq" for length-of-quarter adjustment on quarterly data or transform.adjust="lpyear" for leap year adjustment of monthly or quarterly data (which is only allowed when either transform.power=0 or transform.function="log").

regression.variables:

Object of class "characterOrNULL" - character or character vector representing the names of the regression variables.

regression.user:

Object of class "characterOrNULL" - character or character vector defining the user parameters in the regression argument.

regression.file:

Object of class "characterOrNULL" - path to the file containing the data values of all regression.user variables.

regression.usertype:

Object of class "characterOrNULL" - character or character vector assigning a type of model-estimated regression effect on each user parameter in the regression argument ("seasonal", "td", "lpyear", "user", ...).
By specifying a character vector of length greater one each variable can be given its own type. Otherwise the same type will be used for all user parameters.

regression.centeruser:

Object of class "characterOrNULL" - character specifying the removal of the (sample) mean or the seasonal means from the user parameters in the regression argument ("mean", "seasonal").
Default is no modification of the respective user-defined regressors.

regression.start:

Object of class "numericOrNULLOrcharacter" - start date for the values of the regression.user variables, specified as a vector of two integers in the format c(year, seasonal period).

regression.aictest:

Object of class "characterOrNULL" - character vector defining the regression variables for which an AIC test is to be performed.

outlier.types:

Object of class "characterOrNULL" - to enable the "outlier" specification in the spc file, this parameter has to be defined by a character or character vector determining the method(s) used for outlier detection ("AO", "LS", "TC", "all").

outlier.critical:

Object of class "listOrNULLOrnumeric" - number specifying the critical value used for outlier detection (same value used for all types of outliers) or named list (possible names of list elements being AO,LS and TC) where each list element specifies the respective critical value used for detecting the corresponding type of outlier.
If not specified, the default critical value is used.

outlier.span:

Object of class "numericOrNULLOrcharacter" - vector of length 4, defining the span for outlier detection. Specified in the same way as span.

outlier.method:

Object of class "characterOrNULL" - character determining how detected outliers should be added to the model ("addone", "addall"). If not specified,"addone" is used by default.

identify:

Object of class "logical" - if TRUE, the "identify" specification will be enabled in the spc file.

identify.diff:

Object of class "numericOrNULL" - number or vector representing the orders of nonseasonal differences specified, default is 0.

identify.sdiff:

Object of class "numericOrNULL" - number or vector representing the orders of seasonal differences specified, default is 0.

identify.maxlag:

Object of class "numericOrNULL" - number of lags specified for the ACFs and PACFs, default is 36 for monthly series and 12 for quarterly series.

arima.model:

Object of class "numericOrNULL" - vector of length 3, defining the arima parameters.

arima.smodel:

Object of class "numericOrNULL" - vector of length 3, defining the sarima parameters.

arima.ar:

Object of class "numericOrNULLOrcharacter" - numeric or character vector specifying the initial values for nonseasonal and seasonal autoregressive parameters in the order that they appear in the arima.model argument. Empty positions are created with NA.

arima.ma:

Object of class "numericOrNULLOrcharacter" - numeric or character vector specifying the initial values for all moving average parameters in the order that they appear in the arima.model argument. Empty positions are created with NA.

automdl:

Object of class "logical" - TRUE/FALSE for activating auto modeling.

automdl.acceptdefault:

Object of class "logical" - logical for automdl defining whether the default model should be chosen if the Ljung-Box Q statistic for its model residuals is acceptable.

automdl.balanced:

Object of class "logical" - logical for automdl defining whether the automatic model procedure will tend towards balanced models. TRUE yields the same preference as the TRAMO program.

automdl.maxorder:

Object of class "numeric" - vector of length 2, specifying the maximum order for automdl. Empty positions are created with NA.

automdl.maxdiff:

Object of class "numeric" - vector of length 2, specifying the maximum diff. order for automdl. Empty positions are created with NA.

forecast_years:

Object of class "numericOrNULL" - number of years to forecast, default is 1 year.

backcast_years:

Object of class "numericOrNULL" - number of years to backcast, default is no backcasts.

forecast_conf:

Object of class "numeric" - probability for the confidence interval of forecasts.

forecast_save:

Object of class "character" either "ftr"(in transformed scaling) or "fct"(in original scaling)

estimate:

Object of class "logical" - if TRUE, the term "estimate" will be added to the spc file.

estimate.outofsample:

Object of class "logical" - logical defining whether "out of sample" or "within sample" forecast errors should be used in calculating the average magnitude of forecast errors over the last three years.

check:

Object of class "logical" - TRUE/FALSE for activating the "check" specification in the spc file.

check.maxlag:

Object of class "numericOrNULL" - the number of lags requested for the residual sample ACF and PACF, default is 24 for monthly series and 8 for quarterly series.

slidingspans:

Object of class "logical" - if TRUE, "slidingspans" specification will be enabled in the spc file.

slidingspans.fixmdl:

Object of class "characterOrNULL" - ("yes" (default), "no", "clear").

slidingspans.fixreg:

Object of class "characterOrNULL" - character or character vector specifying the trading day, holiday, outlier or other user-defined regression effects to be fixed ("td", "holiday", "outlier", "user"). All other regression coefficients will be re-estimated for each sliding span.

slidingspans.length:

Object of class "numericOrNULL" - numeric value specifying the length of each span in months or quarters (>3 years, <17 years).

slidingspans.numspans:

Object of class "numericOrNULL" - numeric value specifying the number of sliding spans used to generate output for comparisons (must be between 2 and 4, inclusive).

slidingspans.outlier:

Object of class "characterOrNULL" - ("keep" (default), "remove", "yes").

slidingspans.additivesa:

Object of class "characterOrNULL" - ("difference" (default), "percent").

slidingspans.start:

Object of class "numericOrNULLOrcharacter" - specified as a vector of two integers in the format c(start year, start seasonal period).

history:

if TRUE, the history specification will be enabled.

history.estimates:

Object of class "characterOrNULL" - character or character vector determining which estimates from the regARIMA modeling and/or the x11 seasonal adjustment will be analyzed in the history analysis ("sadj" (default), "sadjchng", "trend", "trendchng", "seasonal", "aic", "fcst").

history.fixmdl:

Object of class "logical" - logical determining whether the regARIMA model will be re-estimated during the history analysis.

history.fixreg:

Object of class "characterOrNULL" - character or character vector specifying the trading day, holiday, outlier or other user-defined regression effects to be fixed ("td", "holiday", "outlier", "user"). All other coefficients will be re-estimated for each history span.

history.outlier:

Object of class "characterOrNULL" - ("keep" (default), "remove", "auto")

history.sadjlags:

Object of class "numericOrNULL" - integer or vector specifying up to 5 revision lags (each >0) that will be analyzed in the revisions analysis of lagged seasonal adjustments.

history.trendlags:

Object of class "numericOrNULL" - integer or vector specifying up to 5 revision lags (each >0) that will be used in the revision history of the lagged trend components.

history.start:

Object of class "numericOrNULLOrcharacter" - specified as a vector of two integers in the format c(start year, start seasonal period).

history.target:

Object of class "characterOrNULL" - character determining whether the revisions of the seasonal adjustments and trends calculated at the lags specified in history.sadjlags and history.trendlags should be defined by the deviation from the concurrent estimate or the deviation from the final estimate ("final" (default), "concurrent").

x11.sigmalim:

Object of class "numericOrNULL" - vector of length 2, defining the limits for sigma in the x11 methodology, used to downweight extreme irregular values in the internal seasonal adjustment iterations.

x11.type:

Object of class "characterOrNULL" - character, i.e. "summary", "trend" or "sa". If x11.type="trend", x11 will only be used to estimate the final trend-cycle as well as the irregular components and to adjust according to trading days. The default setting is type="sa" where a seasonal decomposition of the series is calculated.

x11.sfshort:

Object of class "logical" - logical controlling the seasonal filter to be used if the series is at most 5 years long. If TRUE, the arguments of the seasonalma filter will be used wherever possible. If FALSE, a stable seasonal filter will be used irrespective of seasonalma.

x11.samode:

Object of class "characterOrNULL" - character defining the type of seasonal adjustment decomposition calculated ("mult", "add", "pseudoadd", "logadd").

x11.seasonalma:

Object of class "characterOrNULL" - character or character vector of the format c("snxm","snxm", ...) defining which seasonal nxm moving average(s) should be used for which calendar months or quarters to estimate the seasonal factors. If only one ma is specified, the same ma will be used for all months or quarters. If not specified, the program will invoke an automatic choice.

x11.trendma:

Object of class "numericOrNULL" - integer defining the type of Henderson moving average used for estimating the final trend cycle. If not specified, the program will invoke an automatic choice.

x11.appendfcst:

Object of class "logical" - logical defining whether forecasts should be included in certain x11 tables.

x11.appendbcst:

Object of class "logical" - logical defining whether forecasts should be included in certain x11 tables.

x11.calendarsigma:

Object of class "characterOrNULL" - regulates the way the standard errors used for the detection and adjustment of extreme values should be computed ("all", "signif", "select" or no specification).

x11.excludefcst:

Object of class "logical" - logical defining if forecasts and backcasts from the regARIMA model should not be used in the generation of extreme values in the seasonal adjustment routines.

x11.final:

Object of class "character" - character or character vector specifying which type(s) of prior adjustment factors should be removed from the final seasonally adjusted series ("AO", "LS", "TC", "user", "none").

x11regression:

Object of class "logical" - if TRUE, x11Regression will be performed using the respective regression and outlier commands above, i.e. regression.variables, regression.user, regression.file, regression.usertype, regression.centeruser and regression.start as well as outlier.critical, outlier.span and outlier.method.

Methods

getP

signature(object = "x12Parameter"): ...

setP

signature(object = "x12Parameter"): ...

Author(s)

Alexander Kowarik, Angelika Meraner

Examples

showClass("x12Parameter")

Class "x12Single"

Description

Class consisting of all information for x12.

Objects from the Class

Objects can be created by calls of the form new("x12Single", ...).

Slots

ts:

Object of class ts

x12Parameter:

Object of class x12Parameter-class

x12Output:

Object of class x12Output-class

x12OldParameter:

Object of class list

x12OldOutput:

Object of class list

tsName:

Object of class characterOrNULL

firstRun:

Object of class logical

Methods

setP

signature(object = "x12Single")

getP

signature(object = "x12Single")

prev

signature(object = "x12Single")

cleanArchive

signature(object = "x12Single")

loadP

signature(object = "x12Single")

saveP

signature(object = "x12Single")

summary

signature(object = "x12Single")

x12

signature(object = "x12Single")

plot

signature(object = "x12Single")

crossVal

signature(object = "x12Single")

plotSpec

signature(object = "x12Single")

plotSeasFac

signature(object = "x12Single")

plotRsdAcf

signature(object = "x12Single")

cleanHistory

signature(object = "x12Single")

Note

cleanHistory is deprecated and cleanArchive should be used instead.

Author(s)

Alexander Kowarik

See Also

x12, x12Batch, x12Parameter, x12List, x12Output, x12BaseInfo, summary, getP, x12work

Examples

## Not run: 
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)))
s <- x12(s)

## End(Not run)

Function to interact with the environment x12env

Description

"x12env" is used to store the x12path and x13path (and more for the GUI).

Usage

x12env
x12path(path=NULL)
putd(x,value)
getd(x, mode="any")
rmd(x)
existd(x, mode="any")

Arguments

path

The path to the X12 or X13 binaries.

x

a character for the name

value

value that should be assigned to the object with name x.

mode

the mode or type of object sought

Author(s)

Alexander Kowarik

See Also

get, assign, exists, x12

Examples

  ## Not run: 
  x12path()
  x12path("d:/x12/x12a.exe")
  x12path()
  getd("x12path")
  
## End(Not run)

Run x12 on an R TS-object

Description

A wrapper function for the x12 binaries. It creates a specification file for an R time series and runs x12, afterwards the output is read into R.

Usage

x12work(tso,period=frequency(tso),file="Rout",
  series.span=NULL,series.modelspan=NULL,
 	transform.function="auto",transform.power=NULL,transform.adjust=NULL,
	regression.variables=NULL,regression.user=NULL,regression.file=NULL,
	regression.usertype=NULL,regression.centeruser=NULL,regression.start=NULL,
	regression.aictest=NULL,
	outlier.types=NULL,outlier.critical=NULL,outlier.span=NULL,outlier.method=NULL,
	identify=FALSE,identify.diff=NULL,identify.sdiff=NULL,identify.maxlag=NULL,
	arima.model=NULL,arima.smodel=NULL,arima.ar=NULL,arima.ma=NULL,
	automdl=FALSE,automdl.acceptdefault=FALSE,automdl.balanced=TRUE,
	automdl.maxorder=c(3,2),automdl.maxdiff=c(1,1),
	forecast_years=NULL,backcast_years=NULL,forecast_conf=.95,
	forecast_save="ftr",
	estimate=FALSE,estimate.outofsample=TRUE,
	check=TRUE,check.maxlag=NULL,
	slidingspans=FALSE,
	slidingspans.fixmdl=NULL,slidingspans.fixreg=NULL,
	slidingspans.length=NULL,slidingspans.numspans=NULL,
	slidingspans.outlier=NULL,
	slidingspans.additivesa=NULL,slidingspans.start=NULL,
	history=FALSE,
	history.estimates=NULL,history.fixmdl=FALSE,
	history.fixreg=NULL,history.outlier=NULL,
	history.sadjlags=NULL,history.trendlags=NULL,
	history.start=NULL,history.target=NULL,
	x11.sigmalim=c(1.5,2.5),x11.type=NULL,x11.sfshort=FALSE,x11.samode=NULL,
	x11.seasonalma=NULL,x11.trendma=NULL,
	x11.appendfcst=TRUE,x11.appendbcst=FALSE,x11.calendarsigma=NULL,
	x11.excludefcst=TRUE,x11.final="user",
	x11regression=FALSE,
	tblnames=NULL,Rtblnames=NULL,
	x12path=NULL,use="x12",keep_x12out=TRUE,showWarnings=TRUE)

Arguments

tso

a time series object.

period

frequency of the time series.

file

path to the output directory and filename, default is the working directory and Rout.*.

series.span

vector of length 4, limiting the data used for the calculations and analysis to a certain time interval.
Start and end date of said time interval can be specified by 4 integers in the format c(start year, start seasonal period, end year, end seasonal period) If the start or end date of the time series object should be used, the respective year and seasonal period are to be set to NA.

series.modelspan

vector of length 4, defining the start and end date of the time interval of the data that should be used to determine all regARIMA model coefficients. Specified in the same way as span.

transform.function

transform parameter for x12 ("auto", "log", "none").

transform.power

numeric value specifying the power of the Box Cox power transformation.

transform.adjust

determines the type of adjustment to be performed, i.e. transform.adjust="lom" for length-of-month adjustment on monthly data, transform.adjust="loq" for length-of-quarter adjustment on quarterly data or transform.adjust="lpyear" for leap year adjustment of monthly or quarterly data (which is only allowed when either transform.power=0 or transform.function="log").

regression.variables

character or character vector representing the names of the regression variables.

regression.user

character or character vector defining the user parameters in the regression argument.

regression.file

path to the file containing the data values of all regression.user variables.

regression.usertype

character or character vector assigning a type of model-estimated regression effect on each user parameter in the regression argument ("seasonal", "td", "lpyear", "user", ...).
By specifying a character vector of length greater one each variable can be given its own type. Otherwise the same type will be used for all user parameters.

regression.centeruser

character specifying the removal of the (sample) mean or the seasonal means from the user parameters in the regression argument ("mean", "seasonal").
Default is no modification of the respective user-defined regressors.

regression.start

start date for the values of the regression.user variables, specified as a vector of two integers in the format c(year, seasonal period).

regression.aictest

character vector defining the regression variables for which an AIC test is to be performed.

outlier.types

to enable the "outlier" specification in the spc file, this parameter has to be defined by a character or character vector determining the method(s) used for outlier detection ("AO", "LS", "TC", "all").

outlier.critical

number specifying the critical value used for outlier detection (same value used for all types of outliers) or named list (possible names of list elements being AO,LS and TC) where each list element specifies the respective critical value used for detecting the corresponding type of outlier.
If not specified, the default critical value is used.

outlier.span

vector of length 2, defining the span for outlier detection.

outlier.method

character determining how detected outliers should be added to the model ("addone", "addall"). If not specified,"addone" is used by default.

identify

Object of class "logical" - if TRUE, the "identify" specification will be enabled in the spc file.

identify.diff

number or vector representing the orders of nonseasonal differences specified, default is 0.

identify.sdiff

number or vector representing the orders of seasonal differences specified, default is 0.

identify.maxlag

number of lags specified for the ACFs and PACFs, default is 36 for monthly series and 12 for quarterly series.

arima.model

vector of length 3, defining the arima parameters.

arima.smodel

vector of length 3, defining the sarima parameters.

arima.ar

numeric or character vector specifying the initial values for nonseasonal and seasonal autoregressive parameters in the order that they appear in the arima.model argument. Empty positions are created with NA.

arima.ma

numeric or character vector specifying the initial values for all moving average parameters in the order that they appear in the arima.model argument. Empty positions are created with NA.

automdl

TRUE/FALSE for activating auto modeling.

automdl.acceptdefault

logical for automdl defining whether the default model should be chosen if the Ljung-Box Q statistic for its model residuals is acceptable.

automdl.balanced

logical for automdl defining whether the automatic model procedure will tend towards balanced models. TRUE yields the same preference as the TRAMO program.

automdl.maxorder

vector of length 2, maximum order for automdl. Empty positions are created with NA.

automdl.maxdiff

vector of length 2, maximum diff. order for automdl. Empty positions are created with NA.

forecast_years

number of years to forecast, default is 1 year.

backcast_years

number of years to backcast, default is no backcasts.

forecast_conf

probability for the confidence interval of forecasts

forecast_save

character either "ftr"(in transformed scaling) or "fct"(in original scaling)

estimate

if TRUE, the term "estimate" will be added to the spc file.

estimate.outofsample

logical defining whether "out of sample" or "within sample" forecast errors should be used in calculating the average magnitude of forecast errors over the last three years.

check

TRUE/FALSE for activating the "check" specification in the spc file.

check.maxlag

the number of lags requested for the residual sample ACF and PACF, default is 24 for monthly series and 8 for quarterly series.

slidingspans

if TRUE, "slidingspans" specification will be enabled in the spc file.

slidingspans.fixmdl

("yes" (default), "no", "clear").

slidingspans.fixreg

character or character vector specifying the trading day, holiday, outlier or other user-defined regression effects to be fixed ("td", "holiday", "outlier", "user"). All other regression coefficients will be re-estimated for each sliding span.

slidingspans.length

numeric value specifying the length of each span in months or quarters (>3 years, <17 years).

slidingspans.numspans

numeric value specifying the number of sliding spans used to generate output for comparisons (must be between 2 and 4, inclusive).

slidingspans.outlier

("keep" (default), "remove", "yes").

slidingspans.additivesa

("difference" (default), "percent").

slidingspans.start

specified as a vector of two integers in the format c(start year, start seasonal period).

history

if TRUE, the history specification will be enabled.

history.estimates

character or character vector determining which estimates from the regARIMA modeling and/or the x11 seasonal adjustment will be analyzed in the history analysis ("sadj" (default), "sadjchng", "trend", "trendchng", "seasonal", "aic", "fcst").

history.fixmdl

logical determining whether the regARIMA model will be re-estimated during the history analysis.

history.fixreg

character or character vector specifying the trading day, holiday, outlier or other user-defined regression effects to be fixed ("td", "holiday", "outlier", "user"). All other coefficients will be re-estimated for each history span.

history.outlier

("keep" (default), "remove", "auto")

history.sadjlags

integer or vector specifying up to 5 revision lags (each >0) that will be analyzed in the revisions analysis of lagged seasonal adjustments.

history.trendlags

integer or vector specifying up to 5 revision lags (each >0) that will be used in the revision history of the lagged trend components.

history.start

specified as a vector of two integers in the format c(start year, start seasonal period).

history.target

character determining whether the revisions of the seasonal adjustments and trends calculated at the lags specified in history.sadjlags and history.trendlags should be defined by the deviation from the concurrent estimate or the deviation from the final estimate ("final" (default), "concurrent").

x11.sigmalim

vector of length 2, defining the limits for sigma in the x11 methodology, used to downweight extreme irregular values in the internal seasonal adjustment iterations.

x11.type

character, i.e. "summary", "trend" or "sa". If x11.type="trend", x11 will only be used to estimate the final trend-cycle as well as the irregular components and to adjust according to trading days. The default setting is type="sa" where a seasonal decomposition of the series is calculated.

x11.sfshort

logical controlling the seasonal filter to be used if the series is at most 5 years long. If TRUE, the arguments of the seasonalma filter will be used wherever possible. If FALSE, a stable seasonal filter will be used irrespective of seasonalma.

x11.samode

character defining the type of seasonal adjustment decomposition calculated ("mult", "add", "pseudoadd", "logadd").

x11.seasonalma

character or character vector of the format c("snxm","snxm", ...) defining which seasonal nxm moving average(s) should be used for which calendar months or quarters to estimate the seasonal factors. If only one ma is specified, the same ma will be used for all months or quarters. If not specified, the program will invoke an automatic choice.

x11.trendma

integer defining the type of Henderson moving average used for estimating the final trend cycle. If not specified, the program will invoke an automatic choice.

x11.appendfcst

logical defining whether forecasts should be included in certain x11 tables.

x11.appendbcst

logical defining whether forecasts should be included in certain x11 tables.

x11.calendarsigma

regulates the way the standard errors used for the detection and adjustment of extreme values should be computed ("all", "signif", "select" or no specification).

x11.excludefcst

logical defining if forecasts and backcasts from the regARIMA model should not be used in the generation of extreme values in the seasonal adjustment routines.

x11.final

character or character vector specifying which type(s) of prior adjustment factors should be removed from the final seasonally adjusted series ("AO", "LS", "TC", "user", "none").

x11regression

if TRUE, x11Regression will be performed using the respective regression and outlier commands above, i.e. regression.variables, regression.user, regression.file, regression.usertype, regression.centeruser and regression.start as well as outlier.critical, outlier.span and outlier.method.

tblnames

character vector of additional tables to be read into R.

Rtblnames

character vector naming the additional tables.

x12path

path to the x12 binaries, for example d:\x12a\x12a.exe.

use

"x12" or "x13", at the moment only "x12" is tested properly.

keep_x12out

if TRUE, the output files generated by x12 are stored in the folder "gra" in the output directory and are not deleted at the end of a successful run.

showWarnings

logical defining whether warnings and notes generated by x12 should be returned. Errors will be displayed in any case.

Details

Generates an x12 specification file, runs x12 and reads the output files.

Value

x12work returns an object of class "x12".

The function summary is used to print a summary of the diagnostics results.

An object of class "x12" is a list containing at least the following components:

a1

original time series

d10

final seasonal factors

d11

final seasonally adjusted data

d12

final trend cycle

d13

final irregular components

d16

combined adjustment factors

c17

final weights for irregular component

d9

final replacements for SI ratios

e2

differenced, transformed, seasonally adjusted data

d8

final unmodified SI ratios

b1

prior adjusted original series

forecast

point forecasts with prediction intervals

backcast

point backcasts with prediction intervals

dg

a list containing several seasonal adjustment and regARIMA modeling diagnostics, i.e.:
x11regress, transform, samode, seasonalma, trendma, arimamdl, automdl, regmdl, nout, nautoout, nalmostout, almostoutlier, crit, outlier, userdefined, autooutlier, peaks.seas, peaks.td, id.seas, id.rsdseas, spcrsd, spcori, spcsa, spcirr, q, q2, nmfail, loglikelihood, aic, aicc, bic, hq, aape, autotransform, ifout, res.acf, res.pacf, res.acf2,...

file

path to the output directory and filename

tblnames

tables read into R

Rtblnames

names of tables read into R

Note

Only working with available x12 binaries.

Author(s)

Alexander Kowarik, Angelika Meraner

Source

https://www.census.gov/data/software/x13as.html

References

Alexander Kowarik, Angelika Meraner, Matthias Templ, Daniel Schopfhauser (2014). Seasonal Adjustment with the R Packages x12 and x12GUI. Journal of Statistical Software, 62(2), 1-21. URL http://www.jstatsoft.org/v62/i02/.

See Also

x12, ts, summary.x12work, plot.x12work, x12-methods

Examples

### Examples
data(AirPassengers)
## Not run: 
x12out <- x12work(AirPassengers,x12path=".../x12a.exe",transform.function="auto",
		arima.model=c(0,1,1),arima.smodel=c(0,1,1),regression.variables="lpyear",
		x11.sigmalim=c(2.0,3.0),outlier.types="all",outlier.critical=list(LS=3.5,TC=3),
		x11.seasonalma="s3x3")
summary(x12out)
## End(Not run)