Functions to provide information for all output objects (series, diagnostics,
parameters) available with x13() function.
Value
x13_dictionary() returns a character vector containing the
names of all output objects (series, diagnostics, parameters) available with
the x13() function, whereas x13_full_dictionary() returns a
data.frame with format and description, for all the output objects.
Details
These functions provide lists of output names (series, diagnostics,
parameters) available with the x13() function. These names can be
used to generate customized outputs with the userdefined option of the
x13() function (see examples).
The x13_full_dictionary function provides additional information on
object format and description.
Examples
# \donttest{
# Visualize the dictionary
print(x13_dictionary())
#> [1] "period"
#> [2] "span.start"
#> [3] "span.end"
#> [4] "span.n"
#> [5] "span.missing"
#> [6] "log"
#> [7] "adjust"
#> [8] "likelihood.ll"
#> [9] "likelihood.adjustedll"
#> [10] "likelihood.ssqerr"
#> [11] "likelihood.aic"
#> [12] "likelihood.bic"
#> [13] "likelihood.aicc"
#> [14] "likelihood.bicc"
#> [15] "likelihood.bic2"
#> [16] "likelihood.hannanquinn"
#> [17] "likelihood.nparams"
#> [18] "likelihood.nobs"
#> [19] "likelihood.neffectiveobs"
#> [20] "likelihood.df"
#> [21] "arima.p"
#> [22] "arima.d"
#> [23] "arima.q"
#> [24] "arima.bp"
#> [25] "arima.bd"
#> [26] "arima.bq"
#> [27] "arima.theta(*)"
#> [28] "arima.phi(*)"
#> [29] "arima.btheta(*)"
#> [30] "arima.bphi(*)"
#> [31] "regression.espan.start"
#> [32] "regression.espan.end"
#> [33] "regression.espan.n"
#> [34] "regression.espan.missing"
#> [35] "regression.mean"
#> [36] "regression.nlp"
#> [37] "regression.ntd"
#> [38] "regression.leaster"
#> [39] "regression.nmh"
#> [40] "regression.nout"
#> [41] "regression.nao"
#> [42] "regression.nls"
#> [43] "regression.ntc"
#> [44] "regression.nso"
#> [45] "regression.nusers"
#> [46] "regression.mu"
#> [47] "regression.lp"
#> [48] "regression.td(*)"
#> [49] "regression.td-derived"
#> [50] "regression.td-ftest"
#> [51] "regression.easter"
#> [52] "regression.outlier(*)"
#> [53] "regression.user(*)"
#> [54] "regression.missing(*)"
#> [55] "residuals.res"
#> [56] "residuals.tsres"
#> [57] "residuals.n"
#> [58] "residuals.df"
#> [59] "residuals.dfc"
#> [60] "residuals.ser"
#> [61] "residuals.ser_ml"
#> [62] "residuals.type"
#> [63] "residuals.mean"
#> [64] "residuals.skewness"
#> [65] "residuals.kurtosis"
#> [66] "residuals.doornikhansen"
#> [67] "residuals.lb"
#> [68] "residuals.bp"
#> [69] "residuals.lb2"
#> [70] "residuals.bp2"
#> [71] "residuals.seaslb"
#> [72] "residuals.seasbp"
#> [73] "residuals.nruns"
#> [74] "residuals.lruns"
#> [75] "residuals.nudruns"
#> [76] "residuals.ludruns"
#> [77] "regression.ml.parameters"
#> [78] "regression.ml.pcovar"
#> [79] "regression.ml.pcovar-ml"
#> [80] "regression.ml.pcorr"
#> [81] "regression.ml.pscore"
#> [82] "regression.details.description"
#> [83] "regression.details.type"
#> [84] "regression.details.coefficients"
#> [85] "regression.details.covar"
#> [86] "regression.details.covar-ml"
#> [87] "y"
#> [88] "y_f(?)"
#> [89] "y_ef(?)"
#> [90] "y_b(?)"
#> [91] "y_eb(?)"
#> [92] "yc"
#> [93] "yc_f(?)"
#> [94] "yc_b(?)"
#> [95] "ylin"
#> [96] "ylin_f(?)"
#> [97] "ylin_b(?)"
#> [98] "det"
#> [99] "det_f(?)"
#> [100] "det_b(?)"
#> [101] "cal"
#> [102] "cal_f(?)"
#> [103] "cal_b(?)"
#> [104] "ycal"
#> [105] "ycal_f(?)"
#> [106] "ycal_b(?)"
#> [107] "tde"
#> [108] "tde_f(?)"
#> [109] "tde_b(?)"
#> [110] "ee"
#> [111] "ee_f(?)"
#> [112] "ee_b(?)"
#> [113] "omhe"
#> [114] "omhe_f(?)"
#> [115] "omhe_b(?)"
#> [116] "mhe"
#> [117] "mhe_f(?)"
#> [118] "mhe_b(?)"
#> [119] "out"
#> [120] "out_f(?)"
#> [121] "out_b(?)"
#> [122] "reg"
#> [123] "reg_f(?)"
#> [124] "reg_b(?)"
#> [125] "l"
#> [126] "l_f(?)"
#> [127] "l_b(?)"
#> [128] "full_res"
#> [129] "mode"
#> [130] "seasonal"
#> [131] "sa"
#> [132] "t"
#> [133] "s"
#> [134] "i"
#> [135] "sa_f"
#> [136] "t_f"
#> [137] "s_f"
#> [138] "i_f"
#> [139] "out_t"
#> [140] "out_s"
#> [141] "out_i"
#> [142] "reg_t"
#> [143] "reg_s"
#> [144] "reg_i"
#> [145] "reg_sa"
#> [146] "reg_u"
#> [147] "reg_y"
#> [148] "det_t"
#> [149] "det_s"
#> [150] "det_i"
#> [151] "out_t_f(?)"
#> [152] "out_s_f(?)"
#> [153] "out_i_f(?)"
#> [154] "reg_t_f(?)"
#> [155] "reg_s_f(?)"
#> [156] "reg_i_f(?)"
#> [157] "reg_sa_f(?)"
#> [158] "reg_u_f(?)"
#> [159] "reg_y_f(?)"
#> [160] "det_t_f(?)"
#> [161] "det_s_f(?)"
#> [162] "det_i_f(?)"
#> [163] "out_t_b(?)"
#> [164] "out_s_b(?)"
#> [165] "out_i_b(?)"
#> [166] "reg_t_b(?)"
#> [167] "reg_s_b(?)"
#> [168] "reg_i_b(?)"
#> [169] "reg_sa_b(?)"
#> [170] "reg_u_b(?)"
#> [171] "reg_y_b(?)"
#> [172] "det_t_b(?)"
#> [173] "det_s_b(?)"
#> [174] "det_i_b(?)"
#> [175] "decomposition.y_cmp"
#> [176] "decomposition.y_cmp_f"
#> [177] "decomposition.y_cmp_b"
#> [178] "decomposition.sa_cmp"
#> [179] "decomposition.t_cmp"
#> [180] "decomposition.s_cmp"
#> [181] "decomposition.i_cmp"
#> [182] "preadjustment.a1"
#> [183] "preadjustment.a1a"
#> [184] "preadjustment.a1b"
#> [185] "preadjustment.a6"
#> [186] "preadjustment.a7"
#> [187] "preadjustment.a8"
#> [188] "preadjustment.a8t"
#> [189] "preadjustment.a8i"
#> [190] "preadjustment.a8s"
#> [191] "preadjustment.a9"
#> [192] "preadjustment.a9"
#> [193] "preadjustment.a9cal"
#> [194] "preadjustment.a9u"
#> [195] "preadjustment.a9sa"
#> [196] "preadjustment.a9ser"
#> [197] "decomposition.b1"
#> [198] "decomposition.b2"
#> [199] "decomposition.b3"
#> [200] "decomposition.b4"
#> [201] "decomposition.b5"
#> [202] "decomposition.b6"
#> [203] "decomposition.b7"
#> [204] "decomposition.b8"
#> [205] "decomposition.b9"
#> [206] "decomposition.b10"
#> [207] "decomposition.b11"
#> [208] "decomposition.b13"
#> [209] "decomposition.b17"
#> [210] "decomposition.b20"
#> [211] "decomposition.c1"
#> [212] "decomposition.c2"
#> [213] "decomposition.c4"
#> [214] "decomposition.c5"
#> [215] "decomposition.c6"
#> [216] "decomposition.c7"
#> [217] "decomposition.c9"
#> [218] "decomposition.c10"
#> [219] "decomposition.c11"
#> [220] "decomposition.c13"
#> [221] "decomposition.c17"
#> [222] "decomposition.c20"
#> [223] "decomposition.d1"
#> [224] "decomposition.d2"
#> [225] "decomposition.d4"
#> [226] "decomposition.d5"
#> [227] "decomposition.d6"
#> [228] "decomposition.d7"
#> [229] "decomposition.d8"
#> [230] "decomposition.d9"
#> [231] "decomposition.d10"
#> [232] "decomposition.d11"
#> [233] "decomposition.d12"
#> [234] "decomposition.d13"
#> [235] "decomposition.x11-all"
#> [236] "decomposition.icratio"
#> [237] "decomposition.trend-filter"
#> [238] "decomposition.seasonal-filters"
#> [239] "decomposition.d9-global-msr"
#> [240] "decomposition.d9-msr"
#> [241] "decomposition.d9-msr-table"
#> [242] "finals.d11"
#> [243] "finals.d12"
#> [244] "finals.d13"
#> [245] "finals.d16"
#> [246] "finals.d18"
#> [247] "finals.d11a"
#> [248] "finals.d12a"
#> [249] "finals.d16a"
#> [250] "finals.d18a"
#> [251] "finals.d11b"
#> [252] "finals.d12b"
#> [253] "finals.d16b"
#> [254] "finals.d18b"
#> [255] "finals.e1"
#> [256] "finals.e2"
#> [257] "finals.e3"
#> [258] "finals.e11"
#> [259] "diagnostics.seas-lin-combined"
#> [260] "diagnostics.seas-lin-evolutive"
#> [261] "diagnostics.seas-lin-stable"
#> [262] "diagnostics.seas-si-combined"
#> [263] "diagnostics.seas-si-combined3"
#> [264] "diagnostics.seas-si-evolutive"
#> [265] "diagnostics.seas-si-stable"
#> [266] "diagnostics.seas-res-combined"
#> [267] "diagnostics.seas-res-combined3"
#> [268] "diagnostics.seas-res-evolutive"
#> [269] "diagnostics.seas-res-stable"
#> [270] "diagnostics.seas-sa-combined"
#> [271] "diagnostics.seas-sa-combined3"
#> [272] "diagnostics.seas-sa-evolutive"
#> [273] "diagnostics.seas-sa-stable"
#> [274] "diagnostics.seas-i-combined"
#> [275] "diagnostics.seas-i-combined3"
#> [276] "diagnostics.seas-i-evolutive"
#> [277] "diagnostics.seas-i-stable"
#> [278] "diagnostics.seas-lin-qs"
#> [279] "diagnostics.seas-lin-f"
#> [280] "diagnostics.seas-lin-friedman"
#> [281] "diagnostics.seas-lin-kw"
#> [282] "diagnostics.seas-lin-periodogram"
#> [283] "diagnostics.seas-lin-spectralpeaks"
#> [284] "diagnostics.seas-res-qs"
#> [285] "diagnostics.seas-res-f"
#> [286] "diagnostics.seas-res-friedman"
#> [287] "diagnostics.seas-res-kw"
#> [288] "diagnostics.seas-res-periodogram"
#> [289] "diagnostics.seas-res-spectralpeaks"
#> [290] "diagnostics.seas-sa-qs"
#> [291] "diagnostics.seas-sa-f"
#> [292] "diagnostics.seas-sa-friedman"
#> [293] "diagnostics.seas-sa-kw"
#> [294] "diagnostics.seas-sa-periodogram"
#> [295] "diagnostics.seas-sa-spectralpeaks"
#> [296] "diagnostics.seas-i-qs"
#> [297] "diagnostics.seas-i-f"
#> [298] "diagnostics.seas-i-friedman"
#> [299] "diagnostics.seas-i-kw"
#> [300] "diagnostics.seas-i-periodogram"
#> [301] "diagnostics.seas-i-spectralpeaks"
#> [302] "diagnostics.seas-sa-ac1"
#> [303] "diagnostics.td-res-all"
#> [304] "diagnostics.td-res-last"
#> [305] "diagnostics.td-sa-all"
#> [306] "diagnostics.td-sa-last"
#> [307] "diagnostics.td-i-all"
#> [308] "diagnostics.td-i-last"
#> [309] "diagnostics.fcast-insample-mean"
#> [310] "diagnostics.fcast-outsample-mean"
#> [311] "diagnostics.fcast-outsample-variance"
#> [312] "m-statistics.m1"
#> [313] "m-statistics.m2"
#> [314] "m-statistics.m3"
#> [315] "m-statistics.m4"
#> [316] "m-statistics.m5"
#> [317] "m-statistics.m6"
#> [318] "m-statistics.m7"
#> [319] "m-statistics.m8"
#> [320] "m-statistics.m9"
#> [321] "m-statistics.m10"
#> [322] "m-statistics.m11"
#> [323] "m-statistics.q"
#> [324] "m-statistics.q-m2"
#> [325] "variancedecomposition.cycle"
#> [326] "variancedecomposition.seasonality"
#> [327] "variancedecomposition.irregular"
#> [328] "variancedecomposition.tdh"
#> [329] "variancedecomposition.others"
#> [330] "variancedecomposition.total"
#> [331] "quality.summary"
#> [332] "benchmarking.original"
#> [333] "benchmarking.target"
#> [334] "benchmarking.result"
#> attr(,"class")
#> [1] "JD3_DICTIONARY"
summary(x13_dictionary())
#> Length Class Mode
#> 334 JD3_DICTIONARY character
# first 10 lines
head(x13_full_dictionary(), n = 10)
#> name
#> 1 period
#> 2 span.start
#> 3 span.end
#> 4 span.n
#> 5 span.missing
#> 6 log
#> 7 adjust
#> 8 likelihood.ll
#> 9 likelihood.adjustedll
#> 10 likelihood.ssqerr
#> description detail
#> 1 period of the series
#> 2 start of the considered (partial) series
#> 3 end of the considered (partial) series
#> 4 number of periods in the considered (partial) series
#> 5 number of missing values in the considered (partial) series
#> 6 log-transformtion
#> 7 pre-adjustment for leap year
#> 8 log-likelihood
#> 9 adjusted log-likelihood
#> 10 sum of squares
#> output type fullname
#> 1 java.lang.Integer Normal period
#> 2 jdplus.toolkit.base.api.timeseries.TsPeriod Normal span.start
#> 3 jdplus.toolkit.base.api.timeseries.TsPeriod Normal span.end
#> 4 java.lang.Integer Normal span.n
#> 5 java.lang.Integer Normal span.missing
#> 6 java.lang.Integer Normal log
#> 7 java.lang.String Normal adjust
#> 8 java.lang.Double Normal likelihood.ll
#> 9 java.lang.Double Normal likelihood.adjustedll
#> 10 java.lang.Double Normal likelihood.ssqerr
# For more structured information call `View(x13_full_dictionary())`
# Extract names of output of interest
user_defined_output <- x13_dictionary()[c(65, 95, 135)]
user_defined_output
#> [1] "residuals.kurtosis" "ylin" "sa_f"
# Generate the corresponding output in an estimation
y <- rjd3toolkit::ABS$X0.2.09.10.M
m <- x13(y,"rsa3", userdefined=user_defined_output)
# Retrieve user defined output
tail(m$user_defined$ylin)
#> Mar Apr May Jun Jul Aug
#> 2017 1370.3 1522.6 1452.4 1557.2 1445.5 1303.1
m$user_defined$residuals.kurtosis
#> Value: 3.143851
#> P-Value: 0.5512
m$user_defined$sa_f
#> Jan Feb Mar Apr May Jun Jul Aug
#> 2017
#> 2018 1545.102 1550.995 1544.872 1558.419 1556.812 1546.513 1552.880 1555.686
#> Sep Oct Nov Dec
#> 2017 1559.713 1541.278 1551.031 1550.678
#> 2018
# }