Use this function to get the row indices for the training set.
get_partition_indices(
outcomes,
training_frac = 0.8,
groups = NULL,
group_partitions = NULL
)Vector of row indices for the training set.
vector of outcomes
Fraction of data for training set (default: 0.8). Rows
from the dataset will be randomly selected for the training set, and all
remaining rows will be used in the testing set. Alternatively, if you
provide a vector of integers, these will be used as the row indices for the
training set. All remaining rows will be used in the testing set.
Vector of groups to keep together when splitting the data into
train and test sets. If the number of groups in the training set is larger
than kfold, the groups will also be kept together for cross-validation.
Length matches the number of rows in the dataset (default: NULL).
Specify how to assign groups to the training and
testing partitions (default: NULL). If groups specifies that some
samples belong to group "A" and some belong to group "B", then setting
group_partitions = list(train = c("A", "B"), test = c("B")) will result
in all samples from group "A" being placed in the training set, some
samples from "B" also in the training set, and the remaining samples from
"B" in the testing set. The partition sizes will be as close to
training_frac as possible. If the number of groups in the training set is
larger than kfold, the groups will also be kept together for
cross-validation.
Kelly Sovacool, sovacool@umich.edu
If groups is NULL, uses createDataPartition.
Otherwise, uses create_grouped_data_partition().
Set the seed prior to calling this function if you would like your data partitions to be reproducible (recommended).
training_inds <- get_partition_indices(otu_mini_bin$dx)
train_data <- otu_mini_bin[training_inds, ]
test_data <- otu_mini_bin[-training_inds, ]
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