read_sai() extracts all the information of a SA-item (see details).
Details
A SA-item contains more information than just the results of an estimation.
Full information is extracted with the read_sai() function that
returns a list of 5 objects:
ts: raw time series.referenceSpec: initial specification. Reference when refreshing and relaxing constraints.estimationSpec: specification used for the current estimation.resultSpec: specification containing all parameters stemming fromestimationSpec(fully identified model).results: results of the estimation.
Examples
# Load a Workspace
file <- system.file("workspaces", "workspace_test.xml", package = "rjd3workspace")
# \donttest{
jws <- jws_open(file)
# Select SAProcessing
jsap1 <- jws_sap(jws, 1)
# Select SA-item (as java object)
jsai1 <- jsap_sai(jsap1, 3)
# read SA-item
read_sai(jsai = jsai1)
#> $ts
#> $name
#> [1] "RF0899 (frozen)"
#>
#> $moniker
#> $source
#> [1] ""
#>
#> $id
#> [1] "ddc09f6c-9b45-4f9e-8c91-7d3ebf863a0c"
#>
#> attr(,"class")
#> [1] "JD3_TSMONIKER"
#>
#> $metadata
#> $metadata$`@timestamp`
#> [1] "2025-04-03"
#>
#> $metadata$`@source`
#> [1] "Txt"
#>
#> $metadata$`@id`
#> [1] "demetra://tsprovider/Txt/20111201/SERIES?datePattern=dd%2FMM%2Fyyyy&delimiter=SEMICOLON&file=C%3A%5CUsers%5CYWYD5I%5CDocuments%5C00_RJD3_Developpement%5C00_Quick_Data_Tests%5CData%5CIPI_nace4.csv#seriesIndex=5"
#>
#>
#> $data
#> Jan Feb Mar Apr May Jun Jul
#> 1990 111.98017 106.40722 119.29479 104.06842 111.40162 113.26593 137.68031
#> 1991 113.66007 102.14083 109.93337 114.10433 110.58483 116.18019 149.47288
#> 1992 117.67223 106.85055 119.12183 126.15968 105.85344 136.16431 140.57657
#> 1993 108.33656 102.18422 116.12746 102.92001 92.45556 108.30291 127.72081
#> 1994 94.54971 91.42880 106.16091 99.73296 102.20585 111.96834 124.56344
#> 1995 100.63432 105.50623 118.21418 101.54868 106.28329 121.44511 120.91249
#> 1996 128.39843 119.75416 132.54063 120.06373 122.28072 127.53932 142.82216
#> 1997 111.98399 114.99652 120.61000 123.71057 118.33480 125.00624 136.89582
#> 1998 122.09590 116.93875 125.33340 146.04064 116.84619 127.97769 143.99567
#> 1999 127.56552 121.51214 132.55899 124.56279 115.58122 141.70717 138.17692
#> 2000 117.33993 117.86535 131.70287 116.19777 133.02464 124.97383 128.51653
#> 2001 136.58114 127.47884 136.39675 122.82334 129.16321 135.74880 138.26856
#> 2002 120.62738 106.76884 112.05426 109.38146 116.81737 102.43594 122.20700
#> 2003 127.03074 119.14995 131.98683 121.68155 117.71026 123.73558 133.70584
#> 2004 125.51551 111.23217 123.73557 118.42736 107.01738 131.40606 128.55369
#> 2005 126.08225 119.81587 134.43447 137.88734 122.07844 129.54938 116.77939
#> 2006 116.81535 107.39538 128.00768 110.61096 119.76047 128.23184 117.12287
#> 2007 119.80508 105.93946 123.67290 108.94290 110.85610 124.83011 123.22781
#> 2008 104.76819 130.26636 112.21151 134.05148 121.49767 126.58544 128.99294
#> 2009 95.86592 96.84103 83.93245 73.42534 62.17197 90.50661 103.31763
#> 2010 92.72872 91.63029 113.15865 101.92935 96.23019 110.99885 108.78505
#> 2011 98.22572 103.76875 111.68081 98.45033 114.39733 109.49087 111.77548
#> 2012 101.77256 89.49489 91.16292 101.76190 105.04459 105.55559 114.51839
#> 2013 97.60549 105.12530 105.96804 98.59948 88.96558 97.87360 106.14588
#> 2014 108.32803 104.36544 109.09925 105.73119 102.29739 101.41161 119.78952
#> 2015 94.24561 98.94991 112.70495 88.17863 89.45407 97.22399 94.85769
#> 2016 103.81344 99.79168 87.16486 113.09241 114.52146 118.13172 112.31991
#> 2017 112.81103 121.09764 134.97269 95.63631 119.43010 65.48361 96.53360
#> 2018 108.60152 106.78437 116.87420 108.10013 115.64081 98.39516 120.83236
#> 2019 101.60982 96.41422 104.88769 95.18881 101.19053 105.33432 102.87528
#> 2020 97.31089 95.26518 85.76747 74.17675 56.55460 59.71036 92.64317
#> 2021 104.85271 105.78811 116.71424 114.66855 101.34767 105.14274 89.72527
#> 2022 91.86406 102.90153 113.43710 101.63429 98.42886 90.89587 93.27999
#> 2023 88.59938 93.96608 101.74261 84.75229 86.35975 94.94654 88.88975
#> 2024 88.21640 88.85806 96.89828 99.10996 95.52958 91.57847
#> Aug Sep Oct Nov Dec
#> 1990 52.55658 81.87243 127.59827 121.10457 97.01415
#> 1991 46.07469 114.77328 126.80340 115.12363 111.43670
#> 1992 35.60427 119.10939 117.14055 107.06231 98.61792
#> 1993 31.28009 112.43083 100.44586 99.15789 91.86783
#> 1994 39.55558 108.41405 105.23316 97.22535 104.98378
#> 1995 69.14657 116.58196 113.69861 108.77993 87.25713
#> 1996 74.29661 124.12149 131.62377 108.83783 90.24022
#> 1997 71.55120 115.63636 136.61778 109.01689 95.53962
#> 1998 75.27041 134.49850 137.16171 120.40358 109.80494
#> 1999 78.50741 130.59087 123.28671 118.59456 115.67418
#> 2000 84.83709 134.74701 137.46197 128.33917 110.94990
#> 2001 89.51941 125.06668 121.74332 116.62332 85.09859
#> 2002 69.45571 109.78621 121.75284 103.34083 88.93241
#> 2003 82.65245 126.68890 134.95910 115.47870 103.30540
#> 2004 85.55618 134.36555 121.45620 125.62430 102.41849
#> 2005 91.51707 130.73200 119.71370 118.43659 108.02745
#> 2006 83.41014 118.05962 114.51375 110.11624 100.03320
#> 2007 93.71000 106.39368 127.31772 115.44529 99.33299
#> 2008 81.23834 113.18715 108.71695 85.77463 66.14663
#> 2009 66.03259 110.35779 101.35054 93.21770 71.51158
#> 2010 89.10375 112.31140 109.50830 112.37865 90.54569
#> 2011 95.05654 113.99807 97.76763 97.94501 77.42709
#> 2012 76.82779 107.76669 131.90395 123.59141 81.81006
#> 2013 74.41880 97.31120 117.44245 97.65566 79.82971
#> 2014 78.19819 109.65746 113.51701 92.99477 74.99735
#> 2015 65.06826 103.12478 109.37535 103.90976 82.62499
#> 2016 84.37125 118.20611 116.69092 118.76118 101.17812
#> 2017 86.19279 118.78479 120.00556 120.92885 92.18021
#> 2018 85.71753 98.56561 111.11869 101.45262 70.43689
#> 2019 80.20773 99.27261 108.54179 93.97769 74.77705
#> 2020 61.55135 99.18094 111.83976 98.15058 98.95871
#> 2021 74.30368 104.32938 100.33694 78.76491 104.02580
#> 2022 82.29402 98.72387 83.43379 92.50002 64.37818
#> 2023 74.50032 93.86312 97.33622 81.72218 61.72457
#> 2024
#>
#> attr(,"class")
#> [1] "JD3_TS"
#>
#> $referenceSpec
#> Specification
#>
#> Series
#> Serie span: All
#> Preliminary Check: Yes
#>
#> Estimate
#> Model span: All
#>
#> Tolerance: 1e-07
#>
#> Transformation
#> Function: AUTO
#> AIC difference: -2
#> Adjust: NONE
#>
#> Regression
#> Calendar regressor: TradingDays
#> with Leap Year: Yes
#> AutoAdjust: TRUE
#> Test: REMOVE
#>
#> Easter: STANDARD
#> Duration: 8 (Auto)
#> Test: ADD (Auto)
#>
#> Pre-specified outliers: 0
#> Ramps: No
#>
#> Outliers
#> Detection span: All
#> Outliers type:
#> - AO, critical value : 0 (Auto)
#> - LS, critical value : 0 (Auto)
#> - TC, critical value : 0 (Auto)
#> TC rate: 0.7 (Auto)
#> Method: ADDONE (Auto)
#>
#> ARIMA
#> SARIMA model: (0,1,1) (0,1,1)
#>
#> SARIMA coefficients:
#> theta(1) btheta(1)
#> 0 0
#>
#> Specification X11
#> Seasonal component: Yes
#> Length of the Henderson filter: 0
#> Seasonal filter: FILTER_MSR
#> Boundaries used for extreme values correction :
#> lower_sigma: 1.5
#> upper_sigma: 2.5
#> Nb of forecasts: -1
#> Nb of backcasts: 0
#> Calendar sigma: NONE
#>
#> Benchmarking
#> Is enabled: No
#>
#> $estimationSpec
#> Specification
#>
#> Series
#> Serie span: All
#> Preliminary Check: Yes
#>
#> Estimate
#> Model span: All
#>
#> Tolerance: 1e-07
#>
#> Transformation
#> Function: AUTO
#> AIC difference: -2
#> Adjust: NONE
#>
#> Regression
#> Calendar regressor: TradingDays
#> with Leap Year: Yes
#> AutoAdjust: TRUE
#> Test: REMOVE
#>
#> Easter: STANDARD
#> Duration: 8 (Auto)
#> Test: ADD (Auto)
#>
#> Pre-specified outliers: 0
#> Ramps: No
#>
#> Outliers
#> Detection span: All
#> Outliers type:
#> - AO, critical value : 0 (Auto)
#> - LS, critical value : 0 (Auto)
#> - TC, critical value : 0 (Auto)
#> TC rate: 0.7 (Auto)
#> Method: ADDONE (Auto)
#>
#> ARIMA
#> SARIMA model: (0,1,1) (0,1,1)
#>
#> SARIMA coefficients:
#> theta(1) btheta(1)
#> 0 0
#>
#> Specification X11
#> Seasonal component: Yes
#> Length of the Henderson filter: 0
#> Seasonal filter: FILTER_MSR
#> Boundaries used for extreme values correction :
#> lower_sigma: 1.5
#> upper_sigma: 2.5
#> Nb of forecasts: -1
#> Nb of backcasts: 0
#> Calendar sigma: NONE
#>
#> Benchmarking
#> Is enabled: No
#>
#> $resultSpec
#> Specification
#>
#> Series
#> Serie span: All
#> Preliminary Check: Yes
#>
#> Estimate
#> Model span: All
#>
#> Tolerance: 1e-07
#>
#> Transformation
#> Function: LEVEL
#> AIC difference: -2
#> Adjust: NONE
#>
#> Regression
#> Calendar regressor: TradingDays
#> with Leap Year: Yes
#> AutoAdjust: FALSE
#> Test: NO
#>
#> Easter: STANDARD
#> Duration: 1
#> Test: NO
#> Coef:
#> - Type: ESTIMATED
#> - Value: -4.566495
#>
#> Pre-specified outliers: 6
#> - AO (1990-09-01), coefficient: -30.2223704666036 (ESTIMATED)
#> - TC (2009-03-01), coefficient: -28.1358496347564 (ESTIMATED)
#> - AO (2016-03-01), coefficient: -31.1369678944946 (ESTIMATED)
#> - AO (2017-06-01), coefficient: -49.5183798446616 (ESTIMATED)
#> - TC (2020-03-01), coefficient: -28.3006144995563 (ESTIMATED)
#> - TC (2020-05-01), coefficient: -31.8747397192961 (ESTIMATED)
#> Ramps: No
#>
#> Outliers
#> Is enabled: No
#>
#> ARIMA
#> SARIMA model: (0,1,1) (0,1,1)
#>
#> SARIMA coefficients:
#> theta(1) btheta(1)
#> -0.6253 -0.7067
#>
#> Specification X11
#> Seasonal component: Yes
#> Length of the Henderson filter: 0
#> Seasonal filter: FILTER_MSR
#> Boundaries used for extreme values correction :
#> lower_sigma: 1.5
#> upper_sigma: 2.5
#> Nb of forecasts: -1
#> Nb of backcasts: 0
#> Calendar sigma: NONE
#>
#> Benchmarking
#> Is enabled: No
#>
#> $results
#> NULL
#>
# }