Simplify print output for x13/regarima

[AQLT]

I propose to simplify the print output of the /RegARIMA and seasonal adjustment models. A message with a reference to the summary() method is added (which can be disabled with the summary_info option). This is linked to rjdverse/rjd3toolkit#56.

library(rjd3x13)
y <- rjd3toolkit::ABS$X0.2.09.10.M
reg <- regarima(y)
x13_mod <- x13(y)
summary(reg$result)
#> Log-transformation: yes 
#> SARIMA model: (2,1,1) (0,1,1)
#> 
#> Coefficients
#>           Estimate Std. Error  T-stat Pr(>|t|)    
#> phi(1)     0.34740    0.06502   5.343 1.53e-07 ***
#> phi(2)     0.21733    0.06000   3.622 0.000329 ***
#> theta(1)  -0.69937    0.05115 -13.672  < 2e-16 ***
#> btheta(1) -0.48038    0.06993  -6.869 2.45e-11 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Regression model:
#>                   Estimate Std. Error T-stat Pr(>|t|)    
#> td               0.0023233  0.0006844  3.395 0.000755 ***
#> easter           0.0520113  0.0084894  6.127 2.14e-09 ***
#> TC (2000-06-01)  0.1590340  0.0288578  5.511 6.38e-08 ***
#> AO (2000-07-01) -0.2900774  0.0400551 -7.242 2.26e-12 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> Number of observations: 425, Number of effective observations: 412, Number of parameters: 9
#> Loglikelihood: 746.7517, Adjusted loglikelihood: -2120.875
#> Standard error of the regression (ML estimate): 0.03927991 
#> AIC: 4259.75, AICc: 4260.198, BIC: 4295.939

x13_mod
#> Model: X-13
#> Log-transformation: yes 
#> SARIMA model: (2,1,1) (0,1,1)
#> 
#> SARIMA coefficients:
#>    phi(1)    phi(2)  theta(1) btheta(1) 
#>    0.3474    0.2173   -0.6994   -0.4804 
#> 
#> Regression model:
#>              td          easter TC (2000-06-01) AO (2000-07-01) 
#>        0.002323        0.052011        0.159034       -0.290077 
#> 
#> Seasonal filter: S3X3
#> Trend filter: 23 terms Henderson moving average
#> 
#> For a more detailed output, use the 'summary()' function.

options(summary_info = FALSE)
x13_mod
#> Model: X-13
#> Log-transformation: yes 
#> SARIMA model: (2,1,1) (0,1,1)
#> 
#> SARIMA coefficients:
#>    phi(1)    phi(2)  theta(1) btheta(1) 
#>    0.3474    0.2173   -0.6994   -0.4804 
#> 
#> Regression model:
#>              td          easter TC (2000-06-01) AO (2000-07-01) 
#>        0.002323        0.052011        0.159034       -0.290077 
#> 
#> Seasonal filter: S3X3
#> Trend filter: 23 terms Henderson moving average

summary(reg)
#> Method: RegARIMA 
#> Log-transformation: yes 
#> SARIMA model: (2,1,1) (0,1,1)
#> 
#> Coefficients
#>           Estimate Std. Error  T-stat Pr(>|t|)    
#> phi(1)     0.34740    0.06502   5.343 1.53e-07 ***
#> phi(2)     0.21733    0.06000   3.622 0.000329 ***
#> theta(1)  -0.69937    0.05115 -13.672  < 2e-16 ***
#> btheta(1) -0.48038    0.06993  -6.869 2.45e-11 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Regression model:
#>                   Estimate Std. Error T-stat Pr(>|t|)    
#> td               0.0023233  0.0006844  3.395 0.000755 ***
#> easter           0.0520113  0.0084894  6.127 2.14e-09 ***
#> TC (2000-06-01)  0.1590340  0.0288578  5.511 6.38e-08 ***
#> AO (2000-07-01) -0.2900774  0.0400551 -7.242 2.26e-12 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> Number of observations: 425, Number of effective observations: 412, Number of parameters: 9
#> Loglikelihood: 746.7517, Adjusted loglikelihood: -2120.875
#> Standard error of the regression (ML estimate): 0.03927991 
#> AIC: 4259.75, AICc: 4260.198, BIC: 4295.939

summary(x13_mod) # previous output
#> Model: X-13
#> Log-transformation: yes 
#> SARIMA model: (2,1,1) (0,1,1)
#> 
#> Coefficients
#>           Estimate Std. Error  T-stat Pr(>|t|)    
#> phi(1)     0.34740    0.06502   5.343 1.53e-07 ***
#> phi(2)     0.21733    0.06000   3.622 0.000329 ***
#> theta(1)  -0.69937    0.05115 -13.672  < 2e-16 ***
#> btheta(1) -0.48038    0.06993  -6.869 2.45e-11 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Regression model:
#>                   Estimate Std. Error T-stat Pr(>|t|)    
#> td               0.0023233  0.0006844  3.395 0.000755 ***
#> easter           0.0520113  0.0084894  6.127 2.14e-09 ***
#> TC (2000-06-01)  0.1590340  0.0288578  5.511 6.38e-08 ***
#> AO (2000-07-01) -0.2900774  0.0400551 -7.242 2.26e-12 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> Number of observations: 425, Number of effective observations: 412, Number of parameters: 9
#> Loglikelihood: 746.7517, Adjusted loglikelihood: -2120.875
#> Standard error of the regression (ML estimate): 0.03927991 
#> AIC: 4259.75, AICc: 4260.198, BIC: 4295.939
#> 
#> Decomposition
#> Monitoring and Quality Assessment Statistics: 
#>     M stats
#> m1    0.071
#> m2    0.075
#> m3    2.119
#> m4    0.134
#> m5    1.632
#> m6    0.117
#> m7    0.053
#> m8    0.163
#> m9    0.050
#> m10   0.146
#> m11   0.143
#> q     0.465
#> qm2   0.513
#> 
#> Final filters: 
#> Seasonal filter: S3X3
#> Trend filter: 23 terms Henderson moving average
#> 
#> Diagnostics
#> Relative contribution of the components to the stationary
#> portion of the variance in the original series,
#> after the removal of the long term trend (in %)
#> 
#>            Component
#>  cycle        13.284
#>  seasonal     86.948
#>  irregular     0.752
#>  calendar      0.245
#>  others        0.295
#>  total       101.524
#> 
#> Residual seasonality tests
#>                 P.value
#>  seas.ftest.i     0.998
#>  seas.ftest.sa    0.997
#>  seas.qstest.i    0.001
#>  seas.qstest.sa   0.000
#>  td.ftest.i       0.000
#>  td.ftest.sa      0.000
#> 
#> 
#> Final
#> Last values
#>          series       sa    trend      seas       irr
#> Sep 2016 1393.5 1556.806 1548.194 0.8951022 1.0055624
#> Oct 2016 1497.4 1549.534 1546.816 0.9663552 1.0017571
#> Nov 2016 1684.3 1530.373 1546.901 1.1005815 0.9893151
#> Dec 2016 2850.4 1567.050 1548.130 1.8189590 1.0122211
#> Jan 2017 1428.5 1508.199 1549.876 0.9471559 0.9731099
#> Feb 2017 1092.4 1557.942 1551.664 0.7011816 1.0040459
#> Mar 2017 1370.3 1563.584 1553.159 0.8763842 1.0067116
#> Apr 2017 1522.6 1581.406 1553.982 0.9628142 1.0176473
#> May 2017 1452.4 1553.376 1553.920 0.9349956 0.9996498
#> Jun 2017 1557.2 1556.929 1553.056 1.0001743 1.0024936
#> Jul 2017 1445.5 1527.804 1551.625 0.9461293 0.9846474
#> Aug 2017 1303.1 1546.399 1550.102 0.8426674 0.9976109

[TanguyBarthelemy]

Thank you very much for this pull request. I accept it immediately!