Split the data into a training and a validation sets, givenvthe too large number of observations, in order to determine the best number of clusters for the KAMILA algorithm.
Arguments
- RR_OASI
A data frame containing the all the data.
- PARAM_GLOBAL
A data frame containing the parameters. We use the following parameter:
pct_sample_ts: percentage of observations which build the training set.
- list
List of input data frames.
Value
a tidylist containing the following tidy data frames:
RR_OASI_TS: Training set of categorical data.RR_OASI_VS: Validation set of categorical data.
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
#' )