View source: R/cat2cat_utils.R
cross_c2c | R Documentation |
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$", colnames(df))],
weis = rep(1/length(cols), length(cols)),
na.rm = TRUE
)
df |
'data.frame' like result of the 'cat2cat' function for a specific period. |
cols |
'character' vector default all columns under the regex "wei_.*_c2c". |
weis |
'numeric' vector weighs for columns in the 'cols' argument. By default a vector of the same length as 'cols' argument and with equally spaced probability (summing to 1). |
na.rm |
'logical(1)' if 'NA' values should be omitted, default TRUE. |
'data.frame' with the additional column 'wei_cross_c2c'.
## Not run:
data("occup_small", package = "cat2cat")
data("occup", package = "cat2cat")
data("trans", package = "cat2cat")
occup_old <- occup_small[occup_small$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)
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