seasonal 1.3: A Better Way to Seasonal Adjustment Diagnostics

August 7, 2016
By

(This article was first published on R – usefulr, and kindly contributed to R-bloggers)

The R package seasonal makes it easy to use X-13ARIMA-SEATS, the seasonal adjustment software by the United States Census Bureau. Thanks to the x13binary package, installing it from CRAN is now as easy as installing any other R package:

install.packages("seasonal")

The latest version 1.3 comes with a new udg function and a customizable summary method, which give power users of X-13 a convenient way to check the statistics that are of their interest. For a full list of changes, see the package NEWS.

A generalized way to access diagnostics

Version 1.3 offers a generalized way to access diagnostic statistics. In seasonal, it was always possible to use all options of X-13 and access all output series. Now it is easy to access all diagnostics as well.

The main new function is udg, named after the X-13 output file which it is reading. Consider a simple call to seas (the main function of the seasonal package) that uses the X-11 seasonal adjustment method:

m <- seas(AirPassengers, x11 = "")
udg(m)

The udg function returns a list containing 357 named diagnostics. They are properly type-converted, so they can be directly used for further analysis within R.

If we ask for a specific statistic, such as the popular X-11 M statistics, the result will be simplified to a numeric vector (see ?udg for additional options):

udg(m, c("f3.m01", "f3.m02", "f3.m03", "f3.m04"))

## f3.m01 f3.m02 f3.m03 f3.m04 
##  0.041  0.042  0.000  0.283

There are also some new wrappers for commonly used statistics, such as AICBIC, logLik or qs, which use the udg function.

A customizable summary

The new functionality paves the way for a customizable summary method for seas objects. For example, if we want to add the M statistics for X-11 adjustments to the summary, we can write:

summary(m, stats = c("f3.m01", "f3.m02", "f3.m03", "f3.m04"))

## Call:
## seas(x = AirPassengers, x11 = "")
## 
## Coefficients:
##                     Estimate Std. Error z value Pr(>|z|)    
## Weekday           -0.0029497  0.0005232  -5.638 1.72e-08 ***
## Easter[1]          0.0177674  0.0071580   2.482   0.0131 *  
## AO1951.May         0.1001558  0.0204387   4.900 9.57e-07 ***
## MA-Nonseasonal-01  0.1156204  0.0858588   1.347   0.1781    
## MA-Seasonal-12     0.4973600  0.0774677   6.420 1.36e-10 ***
## ---
## Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
## 
## X11 adj.  ARIMA: (0 1 1)(0 1 1)  Obs.: 144  Transform: log
## AICc: 947.3, BIC: 963.9  QS (no seasonality in final):    0  
## Box-Ljung (no autocorr.): 26.65   Shapiro (normality): 0.9908  
## f3.m01: 0.041  f3.m02: 0.042  f3.m03: 0  f3.m04: 0.283 

Note the new line at the end, which shows the M statistics.

If we want to routinely consider these statistics in our summary, we can set the seas.stats option accordingly:

options(seas.stats = c("f3.m01", "f3.m02", "f3.m03", "f3.m04"))

This will change the default behavior, and

summary(m)

will return the same output as above. To restore the default behavior, set the option back to NULL.

options(seas.stats = NULL)

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