Selection of calendar td regressors

    library("rjd3production")

### Regressor selection

To select a set of regressors, use the `select_td()` function:

    library("rjd3toolkit")
    #>
    #> Attaching package: 'rjd3toolkit'
    #> The following objects are masked from 'package:stats':
    #>
    #>     aggregate, mad
    td_table <- select_td(ABS[, seq_len(3L)])
    #>
    #> Série X0.2.09.10.M en cours... 1/3
    #> Computing spec No_TD ...Done !
    #> Computing spec REG1 ...Done !
    #> Computing spec REG2 ...Done !
    #> Computing spec REG3 ...Done !
    #> Computing spec REG5 ...Done !
    #> Computing spec REG6 ...Done !
    #> Computing spec LY ...Done !
    #> Computing spec REG1_LY ...Done !
    #> Computing spec REG2_LY ...Done !
    #> Computing spec REG3_LY ...Done !
    #> Computing spec REG5_LY ...Done !
    #> Computing spec REG6_LY ...Done !
    #>
    #> Série X0.2.08.10.M en cours... 2/3
    #> Computing spec No_TD ...Done !
    #> Computing spec REG1 ...Done !
    #> Computing spec REG2 ...Done !
    #> Computing spec REG3 ...Done !
    #> Computing spec REG5 ...Done !
    #> Computing spec REG6 ...Done !
    #> Computing spec LY ...Done !
    #> Computing spec REG1_LY ...Done !
    #> Computing spec REG2_LY ...Done !
    #> Computing spec REG3_LY ...Done !
    #> Computing spec REG5_LY ...Done !
    #> Computing spec REG6_LY ...Done !
    #>
    #> Série X0.2.07.10.M en cours... 3/3
    #> Computing spec No_TD ...Done !
    #> Computing spec REG1 ...Done !
    #> Computing spec REG2 ...Done !
    #> Computing spec REG3 ...Done !
    #> Computing spec REG5 ...Done !
    #> Computing spec REG6 ...Done !
    #> Computing spec LY ...Done !
    #> Computing spec REG1_LY ...Done !
    #> Computing spec REG2_LY ...Done !
    #> Computing spec REG3_LY ...Done !
    #> Computing spec REG5_LY ...Done !
    #> Computing spec REG6_LY ...Done !
    print(td_table)
    #>         series regs
    #> 1 X0.2.09.10.M REG6
    #> 2 X0.2.08.10.M REG6
    #> 3 X0.2.07.10.M REG6

### Import/Export

Once the regressors have been selected, you can export the table in `.yaml` format and reimport it later:

    path_td <- tempfile(pattern = "td-table", fileext = ".yaml")
    export_td(td_table, path_td)
    #> The td table will be written at  /tmp/RtmpSaDh3f/td-table979670f17d57.yaml
    td_table2 <- import_td(path = path_td)
    #> The td table will be read at  /tmp/RtmpSaDh3f/td-table979670f17d57.yaml
    waldo::compare(td_table, td_table2)
    #> ✔ No differences

### Assignment

Finally, to assign calendar regressors to a workspace, you can use the `assign_td()` function:

    library("rjd3workspace")
    my_ws <- jws_open("my_workspace")
    assign_td(td_table, my_ws)

### Advanced selection

By default, the regressors proposed are those used by INSEE (and in the SSP). To use custom regressor sets, simply create a modelling context](https://rjdverse.github.io/rjd3toolkit/reference/modelling_context.html) containing as many variables as you want regressor sets and one regressor set per variable.

For example, for the INSEE regressor sets, here is the structure of the corresponding modelling context:

    str(create_insee_context(), max.level = 2L)
    #> List of 2
    #>  $ calendars:List of 1
    #>   ..$ FR:List of 2
    #>   .. ..- attr(*, "class")= chr [1:2] "JD3_CALENDAR" "JD3_CALENDARDEFINITION"
    #>  $ variables:List of 11
    #>   ..$ REG1   :List of 1
    #>   ..$ REG2   :List of 2
    #>   ..$ REG3   :List of 3
    #>   ..$ REG5   :List of 5
    #>   ..$ REG6   :List of 6
    #>   ..$ LY     :List of 1
    #>   ..$ REG1_LY:List of 2
    #>   ..$ REG2_LY:List of 3
    #>   ..$ REG3_LY:List of 4
    #>   ..$ REG5_LY:List of 6
    #>   ..$ REG6_LY:List of 7

Let’s imagine that I only want to test three types of sets:

- a first regressor set (`TD2_TB`) containing two regressors: Thursday, week (and weekend in contrast)
- a second set (`TD3_TB`) containing three regressors: Thursday, week (and weekend in contrast) and leap-year regressor
- a third set (`TD6_TB`) that differentiates between all days of the week

We then create our sets of regressors and create the modelling context:

    series_example <- ABS[, 1L]

    TD2_TB <- calendar_td(
        s = series_example,
        groups = c(1L, 1L, 1L, 2L, 1L, 0L, 0L)
    )

    TD3_TB <- cbind(
        calendar_td(
            s = series_example,
            groups = c(1L, 1L, 1L, 2L, 1L, 0L, 0L)
        ),
        lp_variable(s = series_example)
    )
    colnames(TD3_TB) <- c("group_1", "group_2", "ly")

    TD6_TB <- calendar_td(
        s = series_example,
        groups = c(1L, 2L, 3L, 4L, 5L, 6L, 0L)
    )

    my_regressors_sets <- list(
        TD2_TB = TD2_TB,
        TD3_TB = TD3_TB,
        TD6_TB = TD6_TB
    )
    my_context <- modelling_context(variables = my_regressors_sets)

We can now use our context to select the calendar regressors:

    my_td_table <- select_td(ABS[, seq_len(3L)], context = my_context)
    #>
    #> Série X0.2.09.10.M en cours... 1/3
    #> Computing spec No_TD ...Done !
    #> Computing spec TD2_TB ...Done !
    #> Computing spec TD3_TB ...Done !
    #> Computing spec TD6_TB ...Done !
    #>
    #> Série X0.2.08.10.M en cours... 2/3
    #> Computing spec No_TD ...Done !
    #> Computing spec TD2_TB ...Done !
    #> Computing spec TD3_TB ...Done !
    #> Computing spec TD6_TB ...Done !
    #>
    #> Série X0.2.07.10.M en cours... 3/3
    #> Computing spec No_TD ...Done !
    #> Computing spec TD2_TB ...Done !
    #> Computing spec TD3_TB ...Done !
    #> Computing spec TD6_TB ...Done !
    print(my_td_table)
    #>         series   regs
    #> 1 X0.2.09.10.M TD6_TB
    #> 2 X0.2.08.10.M TD6_TB
    #> 3 X0.2.07.10.M TD6_TB