customParams: Make custom parameters for internal modelling

In MUVR2: Multivariate Methods with Unbiased Variable Selection

Make custom parameters for internal modelling

Description

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.

Usage

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")
)

Arguments

method	PLS or RF (default)
robust	Robustness (slack) criterion for determining min and max knees (defaults to 0.05)
ntreeIn	RF parameter: Number of trees in inner cross-validation loop models (defaults to 150)
ntreeOut	RF parameter: Number of trees in outer (consensus) cross-validation loop models (defaults to 300)
mtryMaxIn	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)
compMax	PLS parameter: Maximum number of PLS components (defaults to 5)
nodes	ann parameter:
threshold	ann parameter:
stepmax	ann parameter:
neuralMaxIn	ann parameter: Maximum number of ANN (defaults to 20)
kernel	svm parameter: kernal function to use, which includes sigmoid, radical, polynomial
nu	svm parameter: ratios of errors allowed in the training set range from 0-1
gamma	svm parameters: needed for "vanilladot","polydot","rbfdot" kernel in svm
degree	svm parameter: needed for polynomial kernel in svm
oneHot	TRUE or FALSE using onehot endcoding or not
NZV	TRUE or FALSE using non-zero variance or not
rfMethod	randomforest method, which includes randomForest and ranger
svmMethod	support vector machine method, which includes svm, ksvm, s
annMethod	artificial neural network method which includes 2 different ann methods

Value

a 'methParam' object

Examples

# 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

MUVR2 documentation built on Sept. 16, 2024, 9:06 a.m.