split-methods | R Documentation |
Returns (invisibly) the object containing train and test observations \bm{y}_{1}, \ldots, \bm{y}_{n}
as well as true class membership \bm{\Omega}_{g}
for the test dataset.
## S4 method for signature 'numeric'
split(p = 0.75, Dataset = data.frame(), class = numeric(), ...)
## S4 method for signature 'list'
split(p = list(), Dataset = data.frame(), class = numeric(), ...)
## ... and for other signatures
p |
see Methods section below. |
Dataset |
a data frame containing dataset |
class |
a column number in |
... |
further arguments to |
Returns an object of class RCLS.chunk
.
signature(p = "numeric")
a number specifying the fraction of observations for training 0.0 \leq p \leq 1.0
. The default value is 0.75
.
signature(p = "list")
a list composed of column number p$type
in Dataset
containing the type membership information followed by the corresponding train p$train
and test p$test
values.
The default value is list()
.
Marko Nagode
## Not run:
data(iris)
# Split dataset into train (75
set.seed(5)
Iris <- split(p = 0.75, Dataset = iris, class = 5)
Iris
# Generate simulated dataset.
N <- 1000
class <- c(rep("A", 0.4 * N), rep("B", 0.2 * N),
rep("C", 0.1 * N), rep("D", 0.05 * N), rep("E", 0.25 * N))
type <- c(rep("train", 0.75 * N), rep("test", 0.25 * N))
n <- 300
Dataset <- data.frame(1:n, sample(class, n))
colnames(Dataset) <- c("y", "class")
# Split dataset into train (60
simulated <- split(p = 0.6, Dataset = Dataset, class = 2)
simulated
# Generate simulated dataset.
Dataset <- data.frame(1:n, sample(class, n), sample(type, n))
colnames(Dataset) <- c("y", "class", "type")
# Split dataset into train and test subsets.
simulated <- split(p = list(type = 3, train = "train",
test = "test"), Dataset = Dataset, class = 2)
simulated
## End(Not run)
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