For stratified resampling, this function can create strata from numeric data and also make non-numeric data more conducive to be used for stratification.
An input vector.
A single number giving the number of bins desired to stratify a numeric stratification variable.
An integer for the number of unique value threshold in the algorithm.
A proportion of data used to determine if a particular group is too small and should be pooled into another group. We do not recommend decreasing this argument below its default of 0.1 because of the dangers of stratifying groups that are too small.
An integer that is used to determine the best number of
percentiles that should be used. The number of bins are based on
For numeric data, if the number of unique levels is less than
nunique, the data are treated as categorical data.
For categorical inputs, the function will find levels of
occur in the data with percentage less than
pool. The values from
these groups will be randomly assigned to the remaining strata (as will
data points that have missing values in
For numeric data with more unique values than
nunique, the data
will be converted to being categorical based on percentiles of the data.
The percentile groups will have no more than 20 percent of the data in
each group. Again, missing values in
x are randomly assigned
A factor vector.
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set.seed(61) x1 <- rpois(100, lambda = 5) table(x1) table(make_strata(x1)) set.seed(554) x2 <- rpois(100, lambda = 1) table(x2) table(make_strata(x2)) # small groups are randomly assigned x3 <- factor(x2) table(x3) table(make_strata(x3)) # `oilType` data from `caret` x4 <- rep(LETTERS[1:7], c(37, 26, 3, 7, 11, 10, 2)) table(x4) table(make_strata(x4)) table(make_strata(x4, pool = 0.1)) table(make_strata(x4, pool = 0.0)) # not enough data to stratify x5 <- rnorm(20) table(make_strata(x5)) set.seed(483) x6 <- rnorm(200) quantile(x6, probs = (0:10)/10) table(make_strata(x6, breaks = 10))
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