Description Usage Arguments Value See Also
Trains a classifier that predicts phenotypic response
for
MDT
objects.
1 2 3 4 5 6 7 8 9 10 11 12 | trainClassifier(mdt, id, mtable_vars = "all", phen_vars = "none",
fill = NA, partitions = caret::createMultiFolds(y = response(mdt), k =
10L, times = 1L), preprocess = function(x, y) function(x) x,
method = "rf", permute = FALSE, validation = TRUE, parallel = FALSE,
verbose = TRUE, .export = NULL, ...)
## S4 method for signature 'MDT'
trainClassifier(mdt, id, mtable_vars = "all",
phen_vars = "none", fill = NA, partitions = caret::createMultiFolds(y =
response(mdt), k = 10L, times = 1L), preprocess = function(x, y) function(x)
x, method = "rf", permute = FALSE, validation = TRUE,
parallel = FALSE, verbose = TRUE, .export = NULL, ...)
|
mdt |
|
id |
Identification name of classifier: |
mtable_vars |
Variables in |
phen_vars |
Variables in |
fill |
How should missing values in |
partitions |
Some |
preprocess |
Some |
method |
a string specifying which classification or regression model
to use. Possible values are found using |
permute |
|
validation |
|
parallel |
|
verbose |
|
.export |
character vector of variables to export.
This can be useful when accessing a variable that isn't defined in the
current environment.
The default value in |
... |
Further arguments to pass on to |
MLGWAS
object.
trainClassifiers
train
createDataPartition
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