This function can create strata from numeric data and make non-numeric data more conducive for stratification.
Arguments
- x
An input vector.
- breaks
A single number giving the number of bins desired to stratify a numeric stratification variable.
- nunique
An integer for the number of unique value threshold in the algorithm.
- pool
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.
- depth
An integer that is used to determine the best number of percentiles that should be used. The number of bins are based on
min(5, floor(n / depth))wheren = length(x). Ifxis numeric, there must be at least 40 rows in the data set (whendepth = 20) to conduct stratified sampling.
Details
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 x than
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 x).
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
to groups.
Examples
set.seed(61)
x1 <- rpois(100, lambda = 5)
table(x1)
#> x1
#> 1 2 3 4 5 6 7 8 9 10 11
#> 3 16 8 19 14 18 11 4 5 1 1
table(make_strata(x1))
#>
#> [1,3] (3,5] (5,6] (6,11]
#> 27 33 18 22
set.seed(554)
x2 <- rpois(100, lambda = 1)
table(x2)
#> x2
#> 0 1 2 3 4
#> 36 34 19 6 5
table(make_strata(x2))
#>
#> 0 1 2
#> 38 40 22
# small groups are randomly assigned
x3 <- factor(x2)
table(x3)
#> x3
#> 0 1 2 3 4
#> 36 34 19 6 5
table(make_strata(x3))
#>
#> 0 1 2
#> 41 35 24
# `oilType` data from `caret`
x4 <- rep(LETTERS[1:7], c(37, 26, 3, 7, 11, 10, 2))
table(x4)
#> x4
#> A B C D E F G
#> 37 26 3 7 11 10 2
table(make_strata(x4))
#>
#> A B E F
#> 40 27 14 15
table(make_strata(x4, pool = 0.1))
#>
#> A B E F
#> 38 29 12 17
table(make_strata(x4, pool = 0.0))
#> Warning: Stratifying groups that make up 0% of the data may be statistically risky.
#> • Consider increasing `pool` to at least 0.1
#>
#> A B C D E F G
#> 37 26 3 7 11 10 2
# not enough data to stratify
x5 <- rnorm(20)
table(make_strata(x5))
#> Warning: The number of observations in each quantile is below the recommended threshold of 20.
#> • Stratification will use 1 breaks instead.
#> Warning: Too little data to stratify.
#> • Resampling will be unstratified.
#>
#> strata1
#> 20
set.seed(483)
x6 <- rnorm(200)
quantile(x6, probs = (0:10) / 10)
#> 0% 10% 20% 30% 40% 50%
#> -2.9114060 -1.4508635 -0.9513821 -0.6257852 -0.3286468 -0.0364388
#> 60% 70% 80% 90% 100%
#> 0.2027140 0.4278573 0.7050643 1.2471852 2.6792505
table(make_strata(x6, breaks = 10))
#>
#> [-2.91,-1.45] (-1.45,-0.951] (-0.951,-0.626] (-0.626,-0.329]
#> 20 20 20 20
#> (-0.329,-0.0364] (-0.0364,0.203] (0.203,0.428] (0.428,0.705]
#> 20 20 20 20
#> (0.705,1.25] (1.25,2.68]
#> 20 20