x12 | R Documentation |
x12
in Package x12 ~~~~ Methods for function x12
in package x12 ~~
x12(object,x12Parameter=new("x12Parameter"),x12BaseInfo=new("x12BaseInfo"),...)
object |
object of class |
x12Parameter |
object of class |
x12BaseInfo |
object of class |
... |
at the moment only forceRun=FALSE |
signature(object = "ts")
signature(object = "x12Single")
signature(object = "x12Batch")
An S4 object of class x12Output-class
if object
is of class ts
An S4 object of class x12Single-class
if object
is of class x12Single-class
An S4 object of class x12Batch-class
if object
is of class x12Batch-class
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.
Alexander Kowarik, Angelika Meraner
https://www.census.gov/data/software/x13as.html
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/.
summary
,
plot
,
x12env
,
setP
,
getP
,
loadP
,
saveP
,
prev
,
cleanArchive
,
crossVal
## 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)
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