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Splitting the continuous from the categorical data

Source: R/mod_catcontvar.R
mod_catcontvar.Rd

Splits the continuous from the categorical data, in order to use a clustering method.

Usage

mod_catcontvar(RR_OASI, RR_OASI_TS, RR_OASI_VS, PARAM_GLOBAL, list = NULL)

Arguments

RR_OASI

A data frame containing all OASI data, whose variables are:

  • year: Year of the pension register extract.

  • age: Age of the individual.

  • age_retire: Effective retirement age.

  • sex: Sex, female if female, 0 if male.

  • nat: Nationality, foreign if 1, 0 if Swiss.

  • resid: Residence, foreign if 1, 0 if Swiss.

  • benef_type1: Old-age type of benefit if 1, 0 otherwise (dummy).

  • benef_type2: Widow type of benefit if 1, 0 otherwise (dummy).

  • benef_type3: Father's orphan type of benefit if 1, 0 otherwise (dummy).

  • benef_type4: Mother's orphan type of benefit if 1, 0 otherwise (dummy).

  • benef_type5: Twice orphan type of benefit if 1, 0 otherwise (dummy).

  • benef_type6: Spouse's compl. type of benefit if 1, 0 otherwise (dummy).

  • benef_type7: Father's child rent type of benefit if 1, 0 otherwise (dummy).

  • benef_type8: Mother's child rent type of benefit if 1, 0 otherwise (dummy).

  • benef_type : Types of benefits type of benefit (categorical).

  • marital_stat1: Divorced marital status if 1, 0 otherwise (dummy).

  • marital_stat2: Single as reference category marital status if 1, 0 otherwise (dummy).

  • marital_stat3: Married marital status if 1, 0 otherwise (dummy).

  • marital_stat4: Widowed marital status if 1, 0 otherwise (dummy).

  • marital_stat: Marital status.

  • splitting: If 1, splitting of the revenues, 0 otherwise.

  • capping: If 1, the pension is capped, 0 otherwise.

  • contrib_m_ind: Total number of OASI contribution months per individual.

  • contrib_y_ageclass: Total number of contribution years per age group.

  • bonus_m_edu: Number of months paid with a bonus for educative tasks.

  • bonus_m_assist: Number of months paid with a bonus for assistance/care tasks.

RR_OASI_TS

Training set containing x% of the data.

RR_OASI_VS

Validation set containing (100 - x)% of the data.

PARAM_GLOBAL

Data frame containing the parameters. We use the following:

  • categ_var: Set of chosen categorical variables.

  • cont_var: Set of chosen continuous variables.

list

List of input data frames.

Value

a tidylist containing the following tidy data frames:

  • CATEG_DF: Contains only categorical variables (factors).

  • CONT_DF : Contains only continuous variables (numeric).

  • CATEG_DF_TS: Contains only categorical variables (factors), training set.

  • CONT_DF_TS : Contains only continuous variables (numeric), training set.

  • CATEG_DF_VS: Contains only categorical variables (factors), validation set.

  • CONT_DF_VS : Contains only continuous variables (numeric), validation set.

References

https://www.geeksforgeeks.org/the-validation-set-approach-in-r-programming/

Author

Layal Christine Lettry

Examples

#' @examples
#' # create random data
#' IND_YEARLY_RR <- structure(
#'   list(
#'     alt = c(
#'       46L, 38L, 14L, 75L, 30L
#'     ),
#'     sex = c(
#'       "f", "f", "f", "f", "m"
#'     ),
#'     nat = c(
#'       "ch", "ch", "ch", "au", "ch"
#'     ),
#'     dom = c(
#'       "ch", "au", "ch", "ch", "ch"
#'     ),
#'     gpr = c(
#'       "rveuve", "renfant_pere_simple",
#'       "rorphelin_pere_simple", "rvieillesse_simple", "renfant_pere_simple"
#'     ),
#'     zv = c(
#'       "geschieden", "geschieden", "ledig", "ledig", "geschieden"
#'     ),
#'     csplit = c(NA, NA, NA, 0L, NA),
#'     cplaf = c(
#'       NA, NA, NA, 0L,
#'       NA
#'     ), jahr = c(2023L, 2023L, 2023L, 2023L, 2023L),
#'     ram = c(
#'       879274L, 2988594L, 5111279L, 8900743L, 1322875L
#'     ),
#'     monatliche_rente = c(
#'       3399L, 2298L, 541L, 2496L, 3894L
#'     ),
#'     age_ret = c(
#'       NA, NA, NA, 68L, NA
#'     ),
#'     eprc = c(
#'       0.0526315789473684, 0.0294117647058824, 0.024390243902439, 0.1, 0.125
#'     ),
#'     lcot = c(
#'       7L, 494L, 209L, 128L, 323L
#'     ),
#'     lcotg = c(
#'       38L,
#'       22L, 13L, 20L, 44L
#'     ),
#'     lbedu = c(
#'       NA, NA, NA, 394L, NA
#'     ),
#'     lbass = c(
#'       NA, NA, NA, 333L, NA
#'     )
#'   ),
#'   class = c("tbl_df", "tbl", "data.frame"),
#'   row.names = c(NA, -5L)
#' )
#' # create RR_OASI
#' tl_mod_prepa_rr <- mod_prepa_rr(IND_YEARLY_RR = IND_YEARLY_RR)
#'
#' # create PARAM_GLOBAL
#' PARAM_GLOBAL_TIDY <- structure(
#'   list(
#'     key = c(
#'       "method_name",
#'       "path_data",
#'       "description",
#'       "pct_sample_ts",
#'       "categ_var",
#'       "cont_var"
#'     ),
#'     value = c(
#'       "kamila",
#'       "",
#'       "Kamila method",
#'       "80",
#'       "sex, nat, resid, benef_type1, benef_type2, benef_type3, benef_type4,
#'        benef_type5,
#'    benef_type6, benef_type7, benef_type8, benef_type, marital_stat1,
#'    marital_stat2,
#'    marital_stat3, marital_stat4, marital_stat, splitting, capping",
#'       "year, aadr, monthly_pension, age, age_retire, scale, contrib_m_ind,
#'    contrib_y_ageclass, bonus_m_edu, bonus_m_assist"
#'     )
#'   ),
#'   class = c("tbl_df", "tbl", "data.frame"),
#'   row.names = c(
#'     NA,
#'     -6L
#'   )
#' )
#'
#' # spread PARAM_GLOBAL_TIB
#' read_param_tib <- function(tib) {
#'   z1 <- tidyr::pivot_wider(tib, names_from = key, values_from = value)
#'   if (identical(dim(z1), c(0L, 0L))) {
#'     return(z1)
#'   }
#'   select(z1, one_of(tib[["key"]]))
#' }
#'
#' PARAM_GLOBAL <- read_param_tib(PARAM_GLOBAL_TIDY) |>
#'   mutate(pct_sample_ts = as.numeric(pct_sample_ts))
#'
#' # Splitting the data into a Training and a Validation sets
#' tl_mod_tsvs <- mod_tsvs(
#'   RR_OASI = tl_mod_prepa_rr$RR_OASI,
#'   PARAM_GLOBAL = PARAM_GLOBAL
#' )
#'
#' mod_catcontvar(
#'   RR_OASI = tl_mod_prepa_rr$RR_OASI,
#'   RR_OASI_TS = tl_mod_tsvs$RR_OASI_TS,
#'   RR_OASI_VS = tl_mod_tsvs$RR_OASI_VS,
#'   PARAM_GLOBAL = PARAM_GLOBAL
#' )