Functions to provide information for all output objects (series, diagnostics,
parameters) available with tramoseats() function.
Value
tramoseats_dictionary() returns a character vector containing the
names of all output objects (series, diagnostics, parameters) available with
the tramoseats() function, whereas tramoseats_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 tramoseats() function. These names can be
used to generate customized outputs with the userdefined option of the
tramoseats() function (see examples).
The tramoseats_full_dictionary function provides additional information on
object format and description.
Examples
# Visualize the dictionary
print(tramoseats_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] "out_t"
#> [130] "out_s"
#> [131] "out_i"
#> [132] "reg_t"
#> [133] "reg_s"
#> [134] "reg_i"
#> [135] "reg_sa"
#> [136] "reg_u"
#> [137] "reg_y"
#> [138] "det_t"
#> [139] "det_s"
#> [140] "det_i"
#> [141] "out_t_f(?)"
#> [142] "out_s_f(?)"
#> [143] "out_i_f(?)"
#> [144] "reg_t_f(?)"
#> [145] "reg_s_f(?)"
#> [146] "reg_i_f(?)"
#> [147] "reg_sa_f(?)"
#> [148] "reg_u_f(?)"
#> [149] "reg_y_f(?)"
#> [150] "det_t_f(?)"
#> [151] "det_s_f(?)"
#> [152] "det_i_f(?)"
#> [153] "out_t_b(?)"
#> [154] "out_s_b(?)"
#> [155] "out_i_b(?)"
#> [156] "reg_t_b(?)"
#> [157] "reg_s_b(?)"
#> [158] "reg_i_b(?)"
#> [159] "reg_sa_b(?)"
#> [160] "reg_u_b(?)"
#> [161] "reg_y_b(?)"
#> [162] "det_t_b(?)"
#> [163] "det_s_b(?)"
#> [164] "det_i_b(?)"
#> [165] "mode"
#> [166] "seasonal"
#> [167] "sa"
#> [168] "t"
#> [169] "s"
#> [170] "i"
#> [171] "sa_f"
#> [172] "t_f"
#> [173] "s_f"
#> [174] "i_f"
#> [175] "decomposition.y_lin"
#> [176] "decomposition.sa_lin"
#> [177] "decomposition.t_lin"
#> [178] "decomposition.s_lin"
#> [179] "decomposition.i_lin"
#> [180] "decomposition.sa_lin_e"
#> [181] "decomposition.t_lin_e"
#> [182] "decomposition.s_lin_e"
#> [183] "decomposition.i_lin_e"
#> [184] "decomposition.y_lin_f"
#> [185] "decomposition.sa_lin_f"
#> [186] "decomposition.t_lin_f"
#> [187] "decomposition.s_lin_f"
#> [188] "decomposition.i_lin_f"
#> [189] "decomposition.y_lin_ef"
#> [190] "decomposition.sa_lin_ef"
#> [191] "decomposition.t_lin_ef"
#> [192] "decomposition.s_lin_ef"
#> [193] "decomposition.i_lin_ef"
#> [194] "decomposition.y_lin_b"
#> [195] "decomposition.sa_lin_b"
#> [196] "decomposition.t_lin_b"
#> [197] "decomposition.s_lin_b"
#> [198] "decomposition.i_lin_b"
#> [199] "decomposition.y_lin_eb"
#> [200] "decomposition.sa_lin_eb"
#> [201] "decomposition.t_lin_eb"
#> [202] "decomposition.s_lin_eb"
#> [203] "decomposition.i_lin_eb"
#> [204] "decomposition.y_cmp"
#> [205] "decomposition.y_cmp_f"
#> [206] "decomposition.y_cmp_b"
#> [207] "decomposition.sa_cmp"
#> [208] "decomposition.t_cmp"
#> [209] "decomposition.s_cmp"
#> [210] "decomposition.i_cmp"
#> [211] "diagnostics.seas-lin-combined"
#> [212] "diagnostics.seas-lin-evolutive"
#> [213] "diagnostics.seas-lin-stable"
#> [214] "diagnostics.seas-si-combined"
#> [215] "diagnostics.seas-si-combined3"
#> [216] "diagnostics.seas-si-evolutive"
#> [217] "diagnostics.seas-si-stable"
#> [218] "diagnostics.seas-res-combined"
#> [219] "diagnostics.seas-res-combined3"
#> [220] "diagnostics.seas-res-evolutive"
#> [221] "diagnostics.seas-res-stable"
#> [222] "diagnostics.seas-sa-combined"
#> [223] "diagnostics.seas-sa-combined3"
#> [224] "diagnostics.seas-sa-evolutive"
#> [225] "diagnostics.seas-sa-stable"
#> [226] "diagnostics.seas-i-combined"
#> [227] "diagnostics.seas-i-combined3"
#> [228] "diagnostics.seas-i-evolutive"
#> [229] "diagnostics.seas-i-stable"
#> [230] "diagnostics.seas-lin-qs"
#> [231] "diagnostics.seas-lin-f"
#> [232] "diagnostics.seas-lin-friedman"
#> [233] "diagnostics.seas-lin-kw"
#> [234] "diagnostics.seas-lin-periodogram"
#> [235] "diagnostics.seas-lin-spectralpeaks"
#> [236] "diagnostics.seas-res-qs"
#> [237] "diagnostics.seas-res-f"
#> [238] "diagnostics.seas-res-friedman"
#> [239] "diagnostics.seas-res-kw"
#> [240] "diagnostics.seas-res-periodogram"
#> [241] "diagnostics.seas-res-spectralpeaks"
#> [242] "diagnostics.seas-sa-qs"
#> [243] "diagnostics.seas-sa-f"
#> [244] "diagnostics.seas-sa-friedman"
#> [245] "diagnostics.seas-sa-kw"
#> [246] "diagnostics.seas-sa-periodogram"
#> [247] "diagnostics.seas-sa-spectralpeaks"
#> [248] "diagnostics.seas-i-qs"
#> [249] "diagnostics.seas-i-f"
#> [250] "diagnostics.seas-i-friedman"
#> [251] "diagnostics.seas-i-kw"
#> [252] "diagnostics.seas-i-periodogram"
#> [253] "diagnostics.seas-i-spectralpeaks"
#> [254] "diagnostics.seas-sa-ac1"
#> [255] "diagnostics.td-res-all"
#> [256] "diagnostics.td-res-last"
#> [257] "diagnostics.td-sa-all"
#> [258] "diagnostics.td-sa-last"
#> [259] "diagnostics.td-i-all"
#> [260] "diagnostics.td-i-last"
#> [261] "diagnostics.fcast-insample-mean"
#> [262] "diagnostics.fcast-outsample-mean"
#> [263] "diagnostics.fcast-outsample-variance"
#> [264] "variancedecomposition.cycle"
#> [265] "variancedecomposition.seasonality"
#> [266] "variancedecomposition.irregular"
#> [267] "variancedecomposition.tdh"
#> [268] "variancedecomposition.others"
#> [269] "variancedecomposition.total"
#> [270] "quality.summary"
#> [271] "benchmarking.original"
#> [272] "benchmarking.target"
#> [273] "benchmarking.result"
#> attr(,"class")
#> [1] "JD3_DICTIONARY"
summary(tramoseats_dictionary())
#> Length Class Mode
#> 273 JD3_DICTIONARY character
# first 10 lines
head(tramoseats_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(tramoseats_full_dictionary())`
# Extract names of output of interest
user_defined_output <- tramoseats_dictionary()[c(65, 95, 135)]
user_defined_output
#> [1] "residuals.kurtosis" "ylin" "reg_sa"
# Generate the corresponding output in an estimation
y <- rjd3toolkit::ABS$X0.2.09.10.M
# \donttest{
m <- tramoseats(y, "rsafull", userdefined=user_defined_output)
# Retrieve user defined output
tail(m$user_defined$ylin)
#> Mar Apr May Jun Jul Aug
#> 2017 1389.905 1492.068 1460.957 1540.453 1468.642 1279.390
m$user_defined$residuals.kurtosis
#> Value: 3.495232
#> P-Value: 0.0402
m$user_defined$sa_f
#> NULL
# }