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Make custom parameters for MUVR internal modelling, not rdCV. Please note that, at present, there is no mtryMax for the outer (consensus) loop in effect.
customParams(
method = c("RF", "PLS", "SVM", "ANN"),
robust = 0.05,
ntreeIn = 150,
ntreeOut = 300,
mtryMaxIn = 150,
compMax = 5,
nodes = 200,
threshold = 0.1,
stepmax = 1e+08,
neuralMaxIn = 10,
kernel = "notkernel",
nu = 0.1,
gamma = 1,
degree = 1,
oneHot,
NZV,
rfMethod = c("randomForest", "ranger"),
svmMethod = c("svm", "ksvm", "svmlight"),
annMethod = c("nnet", "neuralnet")
)a `methParam` object
PLS or RF (default)
Robustness (slack) criterion for determining min and max knees (defaults to 0.05)
RF parameter: Number of trees in inner cross-validation loop models (defaults to 150)
RF parameter: Number of trees in outer (consensus) cross-validation loop models (defaults to 300)
RF parameter: Max number of variables to sample from at each node in the inner CV loop (defaults to 150). Will be further limited by standard RF rules (see randomForest documentation)
PLS parameter: Maximum number of PLS components (defaults to 5)
ann parameter:
ann parameter:
ann parameter:
ann parameter: Maximum number of ANN (defaults to 20)
svm parameter: kernal function to use, which includes sigmoid, radical, polynomial
svm parameter: ratios of errors allowed in the training set range from 0-1
svm parameters: needed for "vanilladot","polydot","rbfdot" kernel in svm
svm parameter: needed for polynomial kernel in svm
TRUE or FALSE using onehot endcoding or not
TRUE or FALSE using non-zero variance or not
randomforest method, which includes randomForest and ranger
support vector machine method, which includes svm, ksvm, s
artificial neural network method which includes 2 different ann methods
# Standard parameters for random forest
methParam <- customParams() # or
methParam <- customParams('RF')
# Custom ntreeOut parameters for random forest
methParam <- customParams('RF',ntreeOut=50) # or
methParam <- customParams('RF')
methParam$ntreeOut <- 50
methParam
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