MUVR2 (version 0.1.0)

customParams: 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")
)

Value

a `methParam` object

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

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