cross_c2c {cat2cat}

Make a combination of weights from different methods

Description

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.

Usage

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

## Not run: 
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"
)

## End(Not run)