Description Usage Arguments Details Value Note Author(s) Source References See Also Examples
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34  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)

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 
series.span 
vector of length 4, limiting the data used for the calculations and analysis to a certain time interval. 
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 
transform.function 
transform parameter for x12 ( 
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. 
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.usertype 
character or character vector assigning a type of modelestimated regression effect
on each user parameter in the regression argument ( 
regression.centeruser 
character specifying the removal of the (sample) mean or the seasonal means from
the user parameters in the regression argument ( 
regression.start 
start date for the values of the 
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 ( 
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 
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 ( 
identify 
Object of class 
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.ma 
numeric or character vector specifying the initial values for all moving average parameters in the order that they appear in the 
automdl 

automdl.acceptdefault 
logical for 
automdl.balanced 
logical for 
automdl.maxorder 
vector of length 2, maximum order for 
automdl.maxdiff 
vector of length 2, maximum diff. order for 
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 
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 

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 
slidingspans.fixmdl 
( 
slidingspans.fixreg 
character or character vector specifying the trading day, holiday, outlier or other userdefined regression effects to be fixed ( 
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 
( 
slidingspans.additivesa 
( 
slidingspans.start 
specified as a vector of two integers in the format 
history 
if 
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 ( 
history.fixmdl 
logical determining whether the regARIMA model will be reestimated during the history analysis. 
history.fixreg 
character or character vector specifying the trading day, holiday, outlier or other userdefined regression effects to be fixed ( 
history.outlier 
( 
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 
history.target 
character determining whether the revisions of the seasonal adjustments and trends calculated at the lags specified in 
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. 
x11.sfshort 
logical controlling the seasonal filter to be used if the series is at most 5 years long.
If 
x11.samode 
character defining the type of seasonal adjustment decomposition calculated
( 
x11.seasonalma 
character or character vector of the format 
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 ( 
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 ( 
x11regression 
if 
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 
use 

keep_x12out 
if 
showWarnings 
logical defining whether warnings and notes generated by x12 should be returned. Errors will be displayed in any case. 
Generates an x12 specification file, runs x12 and reads the output files.
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.: 
file 
path to the output directory and filename 
tblnames 
tables read into R 
Rtblnames 
names of tables read into R 
Only working with available x12 binaries.
Alexander Kowarik, Angelika Meraner
https://www.census.gov/srd/www/x13as/
Alexander Kowarik, Angelika Meraner, Matthias Templ, Daniel Schopfhauser (2014). Seasonal Adjustment with the R Packages x12 and x12GUI. Journal of Statistical Software, 62(2), 121. URL http://www.jstatsoft.org/v62/i02/.
x12
,
ts
,
summary.x12work
,
plot.x12work
,
x12methods
1 2 3 4 5 6 7 8 9  ### 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)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.