Make a combination of weights from different methods

adding the additional column which is a mix of weights columns by each row. Ensemble of a few methods usually produces more accurate solutions than a single model would.

cross_c2c(
  df,
  cols = colnames(df)[grepl("^wei_.*_c2c$", colnames(df))],
  weis = rep(1/length(cols), length(cols)),
  na.rm = TRUE
)

Arguments

df

`data.frame` like result of the `cat2cat` function for a specific period.

cols

`character` vector default all columns under the regex "wei_.*_c2c".

weis

`numeric` vector weighs for columns in the `cols` argument. By default a vector of the same length as `cols` argument and with equally spaced probability (summing to 1).

na.rm

`logical(1)` if `NA` values should be omitted, default TRUE.

Value

`data.frame` with the additional column `wei_cross_c2c`.

Examples

if (FALSE) {
data("occup_small", package = "cat2cat")
data("occup", package = "cat2cat")
data("trans", package = "cat2cat")

occup_old <- occup_small[occup_small$year == 2008, ]
occup_new <- occup_small[occup_small$year == 2010, ]

# mix of methods - forward direction, try out backward too
occup_mix <- cat2cat(
  data = list(
    old = occup_old, new = occup_new, cat_var = "code", time_var = "year"
  ),
  mappings = list(trans = trans, direction = "backward"),
  ml = list(
    data = occup_new,
    cat_var = "code",
    method = c("knn"),
    features = c("age", "sex", "edu", "exp", "parttime", "salary"),
    args = list(k = 10, ntree = 20)
  )
)
# correlation between ml model
occup_mix_old <- occup_mix$old
cor(
  occup_mix_old[occup_mix_old$rep_c2c != 1, c("wei_knn_c2c", "wei_freq_c2c")]
)
# cross all methods and subset one highest probability category for each obs
occup_old_highest1_mix <- prune_c2c(cross_c2c(occup_mix$old),
  column = "wei_cross_c2c", method = "highest1"
)
}