Create A rqTList Object by Repeated Calls to rq

Create a qrList object by calling rq for each of the probabilities given in tau, using the same formula and data for all fits.

Usage

rqTList(
  formula = TX ~ Cst + cosj1 + sinj1 + cosj2 + sinj2 + cosj3 + sinj3 - 1,
  dailyMet,
  tau = c(0.5, 0.7, 0.8, 0.9, 0.95, 0.97, 0.98, 0.99),
  design = list(trigo = list(what = "tsDesign", args = list(type = "trigo", df = 7))),
  trace = 0
)

Arguments

formula: The formula that will be used in rq.
dailyMet: An object with class "dailyMet" that contains the variables that will be used in the formula or in the design function specified design.
tau: A vector of probabilities.
design: A list to be passed to the designVars function. Each element of this list is itself a named list with an element what describing the "design function" to call, and an argument args describing the arguments to be passed to the design function, except its first argument. For each element of the design list, a design matrix X is obtained by using do.call and the columns of X are added to the data frame given in dailyMet before fiiting the model. The default creates seven trigonometric basis functions for the first three harmonics of the yearly seasonality.
trace: Integer level of verbosity.

Value

An object with class "rqTList".

Details

When extradesign is used, any design function used should fulfill the following requirements.

The first argument must be the date. The name of this argument will not be used in the call.
The returned value should be an object inheriting from the "matrix" class with suitable column names. It can also be a list containing the design matrix as its element named "X".

The call to a design function should mention Date as the first argument. This call refers to the dailyMet object used, from which the Date column will be used.

Examples

Rq <- rqTList(dailyMet = Rennes)
coef(Rq)
#>               Cst     cosj1     sinj1       cosj2     sinj2       cosj3
#> tau=0.50 16.06393 -6.799077 -2.591552 -0.25845982 0.8639527 -0.03092440
#> tau=0.70 17.95239 -7.088085 -2.512324 -0.05437666 0.8490012  0.09359081
#> tau=0.80 19.16725 -7.533421 -2.492802 -0.04192276 0.8351121  0.17707189
#> tau=0.90 20.91793 -8.261868 -2.391511 -0.10477575 0.8021272  0.26742011
#> tau=0.95 22.24216 -8.813697 -2.265968 -0.24878473 0.8232536  0.24753280
#> tau=0.97 23.12972 -9.185094 -2.350450 -0.41137561 0.9621417  0.32897144
#> tau=0.98 23.81042 -9.465539 -2.349878 -0.47458855 1.0689253  0.25157101
#> tau=0.99 24.73573 -9.763231 -2.296899 -0.50022134 1.1528784  0.24505818
#>                sinj3
#> tau=0.50  0.06418181
#> tau=0.70  0.06247610
#> tau=0.80  0.04022597
#> tau=0.90 -0.05661638
#> tau=0.95 -0.09498655
#> tau=0.97 -0.17853494
#> tau=0.98 -0.26641707
#> tau=0.99 -0.27323854
autoplot(Rq)


if (require("NSGEV")) {
 Rq1 <-
   rqTList(formula = TX ~ Cst + cosj1 + sinj1 + cosj2 + sinj2 + t1_1970 - 1,
           dailyMet = Rennes,
           design = list("trigo" = list(what = "tsDesign",
                                        args = list(type = "trigo", df = 7)),
                         "breaks"= list(what = "NSGEV::breaksX",
                                        args = list(breaks = c('1970-01-01', '1990-01-01')))))
 p1 <- predict(Rq1,
               newdata = data.frame(Date = seq(from = as.Date("2024-01-01"),
                                    to = as.Date("2054-01-01"),
                                    by = "day")))
 autoplot(p1)
}