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rjdverse / rjd3tramoseats

R access to Tramo-Seats algorithm in JDemetra+ version 3.x
https://rjdverse.github.io/rjd3tramoseats/
European Union Public License 1.2
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Simplify print output for tramo/tramoseats #49

Closed AQLT closed 3 weeks ago

AQLT commented 1 month ago

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_infooption). This is linked to https://github.com/rjdverse/rjd3toolkit/pull/56 and #36.

library(rjd3tramoseats)
y <- rjd3toolkit::ABS$X0.2.09.10.M
reg <- tramo(y)
ts_mod <- tramoseats(y)
reg
#> Method: TRAMO
#> Log-transformation: yes 
#> SARIMA model: (0,1,1) (0,1,1)
#> 
#> SARIMA coefficients:
#>  theta(1) btheta(1) 
#>   -0.8278   -0.4255 
#> 
#> Regression model:
#>          monday         tuesday       wednesday        thursday          friday 
#>      -0.0109446       0.0048940       0.0001761       0.0132928      -0.0024801 
#>        saturday              lp          easter AO (2000-06-01) AO (2000-07-01) 
#>       0.0153509       0.0410667       0.0503888       0.1681662      -0.1972348 
#> 
#> For a more detailed output, use the 'summary()' function.
summary(reg)
#> Method: TRAMO 
#> Log-transformation: yes 
#> SARIMA model: (0,1,1) (0,1,1)
#> 
#> Coefficients
#>           Estimate Std. Error  T-stat Pr(>|t|)    
#> theta(1)  -0.82783    0.02571 -32.196  < 2e-16 ***
#> btheta(1) -0.42554    0.06388  -6.661 9.01e-11 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Regression model:
#>                   Estimate Std. Error T-stat Pr(>|t|)    
#> monday          -0.0109446  0.0034805 -3.145 0.001788 ** 
#> tuesday          0.0048940  0.0035307  1.386 0.166481    
#> wednesday        0.0001761  0.0034970  0.050 0.959867    
#> thursday         0.0132928  0.0035330  3.763 0.000193 ***
#> friday          -0.0024801  0.0035383 -0.701 0.483748    
#> saturday         0.0153509  0.0035171  4.365 1.62e-05 ***
#> lp               0.0410667  0.0101178  4.059 5.94e-05 ***
#> easter           0.0503888  0.0072698  6.931 1.69e-11 ***
#> AO (2000-06-01)  0.1681662  0.0299743  5.610 3.78e-08 ***
#> AO (2000-07-01) -0.1972348  0.0298664 -6.604 1.28e-10 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> Number of observations: 425, Number of effective observations: 412, Number of parameters: 13
#> Loglikelihood: 781.358, Adjusted loglikelihood: -2086.269
#> Standard error of the regression (ML estimate): 0.03615788 
#> AIC: 4198.538, AICc: 4199.452, BIC: 4250.811
ts_mod
#> Model: TRAMO-SEATS
#> Log-transformation: yes 
#> SARIMA model: (0,1,1) (0,1,1)
#> 
#> SARIMA coefficients:
#>  theta(1) btheta(1) 
#>   -0.8278   -0.4255 
#> 
#> Regression model:
#>          monday         tuesday       wednesday        thursday          friday 
#>      -0.0109446       0.0048940       0.0001761       0.0132928      -0.0024801 
#>        saturday              lp          easter AO (2000-06-01) AO (2000-07-01) 
#>       0.0153509       0.0410667       0.0503888       0.1681662      -0.1972348 
#> 
#> For a more detailed output, use the 'summary()' function.
options(summary_info = FALSE) # to remove the last line
ts_mod
#> Model: TRAMO-SEATS
#> Log-transformation: yes 
#> SARIMA model: (0,1,1) (0,1,1)
#> 
#> SARIMA coefficients:
#>  theta(1) btheta(1) 
#>   -0.8278   -0.4255 
#> 
#> Regression model:
#>          monday         tuesday       wednesday        thursday          friday 
#>      -0.0109446       0.0048940       0.0001761       0.0132928      -0.0024801 
#>        saturday              lp          easter AO (2000-06-01) AO (2000-07-01) 
#>       0.0153509       0.0410667       0.0503888       0.1681662      -0.1972348

summary(reg)
#> Method: TRAMO 
#> Log-transformation: yes 
#> SARIMA model: (0,1,1) (0,1,1)
#> 
#> Coefficients
#>           Estimate Std. Error  T-stat Pr(>|t|)    
#> theta(1)  -0.82783    0.02571 -32.196  < 2e-16 ***
#> btheta(1) -0.42554    0.06388  -6.661 9.01e-11 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Regression model:
#>                   Estimate Std. Error T-stat Pr(>|t|)    
#> monday          -0.0109446  0.0034805 -3.145 0.001788 ** 
#> tuesday          0.0048940  0.0035307  1.386 0.166481    
#> wednesday        0.0001761  0.0034970  0.050 0.959867    
#> thursday         0.0132928  0.0035330  3.763 0.000193 ***
#> friday          -0.0024801  0.0035383 -0.701 0.483748    
#> saturday         0.0153509  0.0035171  4.365 1.62e-05 ***
#> lp               0.0410667  0.0101178  4.059 5.94e-05 ***
#> easter           0.0503888  0.0072698  6.931 1.69e-11 ***
#> AO (2000-06-01)  0.1681662  0.0299743  5.610 3.78e-08 ***
#> AO (2000-07-01) -0.1972348  0.0298664 -6.604 1.28e-10 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> Number of observations: 425, Number of effective observations: 412, Number of parameters: 13
#> Loglikelihood: 781.358, Adjusted loglikelihood: -2086.269
#> Standard error of the regression (ML estimate): 0.03615788 
#> AIC: 4198.538, AICc: 4199.452, BIC: 4250.811
summary(ts_mod) # previous output
#> Model: TRAMO-SEATS
#> Log-transformation: yes 
#> SARIMA model: (0,1,1) (0,1,1)
#> 
#> Coefficients
#>           Estimate Std. Error  T-stat Pr(>|t|)    
#> theta(1)  -0.82783    0.02571 -32.196  < 2e-16 ***
#> btheta(1) -0.42554    0.06388  -6.661 9.01e-11 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Regression model:
#>                   Estimate Std. Error T-stat Pr(>|t|)    
#> monday          -0.0109446  0.0034805 -3.145 0.001788 ** 
#> tuesday          0.0048940  0.0035307  1.386 0.166481    
#> wednesday        0.0001761  0.0034970  0.050 0.959867    
#> thursday         0.0132928  0.0035330  3.763 0.000193 ***
#> friday          -0.0024801  0.0035383 -0.701 0.483748    
#> saturday         0.0153509  0.0035171  4.365 1.62e-05 ***
#> lp               0.0410667  0.0101178  4.059 5.94e-05 ***
#> easter           0.0503888  0.0072698  6.931 1.69e-11 ***
#> AO (2000-06-01)  0.1681662  0.0299743  5.610 3.78e-08 ***
#> AO (2000-07-01) -0.1972348  0.0298664 -6.604 1.28e-10 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> Number of observations: 425, Number of effective observations: 412, Number of parameters: 13
#> Loglikelihood: 781.358, Adjusted loglikelihood: -2086.269
#> Standard error of the regression (ML estimate): 0.03615788 
#> AIC: 4198.538, AICc: 4199.452, BIC: 4250.811
#> 
#> Decomposition
#> model 
#> 
#> DIF: 1 -1 0 0 0 0 0 0 0 0 0 0 -1 1 
#> MA: 1 -0.8278316 0 0 0 0 0 0 0 0 0 0 -0.4255409 0.3522762 
#> var:  1 
#> 
#> trend 
#> 
#> DIF: 1 -2 1 
#> MA: 1 0.06437327 -0.9356267 
#> var:  0.004098184 
#> 
#> seasonal 
#> 
#> DIF: 1 1 1 1 1 1 1 1 1 1 1 1 
#> MA: 1 0.6197472 0.3121522 0.07325877 -0.1026014 -0.2222985 -0.2934508 -0.3240296 -0.3220203 -0.295141 -0.2506158 -0.1950007 
#> var:  0.1460322 
#> 
#> irregular 
#> 
#> var:  0.384587 
#> 
#> 
#> 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         0.250
#>  seasonal     97.046
#>  irregular     0.600
#>  calendar      0.741
#>  others        0.325
#>  total        98.962
#> 
#> Residual seasonality tests
#>                 P.value
#>  seas.ftest.i         1
#>  seas.ftest.sa        1
#>  seas.qstest.i        1
#>  seas.qstest.sa       1
#>  td.ftest.i           1
#>  td.ftest.sa          1
#> 
#> 
#> Final
#> Last values
#>          series       sa    trend      seas       irr
#> Sep 2016 1393.5 1552.616 1561.206 0.8975174 0.9944979
#> Oct 2016 1497.4 1568.366 1559.217 0.9547514 1.0058681
#> Nov 2016 1684.3 1528.962 1557.382 1.1015974 0.9817508
#> Dec 2016 2850.4 1542.997 1556.132 1.8473143 0.9915588
#> Jan 2017 1428.5 1545.950 1555.502 0.9240275 0.9938587
#> Feb 2017 1092.4 1551.369 1555.210 0.7041521 0.9975303
#> Mar 2017 1370.3 1553.207 1555.087 0.8822391 0.9987913
#> Apr 2017 1522.6 1580.752 1554.759 0.9632123 1.0167187
#> May 2017 1452.4 1554.517 1553.908 0.9343093 1.0003924
#> Jun 2017 1557.2 1551.804 1552.778 1.0034774 0.9993726
#> Jul 2017 1445.5 1544.701 1551.717 0.9357801 0.9954781
#> Aug 2017 1303.1 1535.588 1550.949 0.8485999 0.9900960
TanguyBarthelemy commented 3 weeks ago

As your PR in {rjd3xr13}, I accept and merge your modifications. Thank you.