View source: R/04_CUM_BINNING.R
cum.bin | R Documentation |
cum.bin
implements monotonic binning based on maximum cumulative target rate.
This algorithm is known as MAPA (Monotone Adjacent Pooling Algorithm).
cum.bin( x, y, sc = c(NA, NaN, Inf, -Inf), sc.method = "together", g = 15, y.type = NA, force.trend = NA )
x |
Numeric vector to be binned. |
y |
Numeric target vector (binary or continuous). |
sc |
Numeric vector with special case elements. Default values are |
sc.method |
Define how special cases will be treated, all together or in separate bins.
Possible values are |
g |
Number of starting groups. Default is 15. |
y.type |
Type of |
force.trend |
If the expected trend should be forced. Possible values: |
The command cum.bin
generates a list of two objects. The first object, data frame summary.tbl
presents a summary table of final binning, while x.trans
is a vector of discretized values.
In case of single unique value for x
or y
in complete cases (cases different than special cases),
it will return data frame with info.
suppressMessages(library(monobin)) data(gcd) amount.bin <- cum.bin(x = gcd$amount, y = gcd$qual) amount.bin[[1]] gcd$amount.bin <- amount.bin[[2]] gcd %>% group_by(amount.bin) %>% summarise(n = n(), y.avg = mean(qual)) #increase default number of groups (g = 20) amount.bin.1 <- cum.bin(x = gcd$amount, y = gcd$qual, g = 20) amount.bin.1[[1]] #force trend to decreasing cum.bin(x = gcd$amount, y = gcd$qual, g = 20, force.trend = "d")[[1]]
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