Universal fit function (Parallel)

fit.parallel(data, dep, indep, classifier = "lr",
  classifier.params = list(rf.ntree = 100, rf.mtry = NULL, c5.0.trials =
  40, c5.0.rules = TRUE, c5.0.winnow = FALSE, nb.fL = 0, nb.adjust = 1,
  svm.gamma = NULL, svm.cost = 1), params.tuning = FALSE,
  normalize = "no", rebalance = "no", validation = "boot",
  validation.params = list(cv.k = 10, boot.n = 100),
  prob.threshold = 0.5, repeats = 1, n.cores = 2)

Arguments

data

a dataframe for input data

dep

a character for dependent variable

indep

a vector of characters for independent variables

classifier

a character for classifier techniques, i.e., lr, rf, c5.0, nb, and svm

classifier.params

a list of parameters for an input classifier technique

params.tuning

a boolean indicates whether to perform parameters tuning

normalize

a character for normalization techniques, i.e., log, scale, center, standardize, and no for non-normalization#'

rebalance

a character for a choice of data sampling techniques, i.e., up for upsampling, down for downsampling, and no for no-sampling (default: "NO")

validation

a character for a choice of validation techniques, i.e., boot for bootstrap validation technique, cv for cross-validation technique, and no for constructing a model with the whole dataset without model validation (default: "boot")

validation.params

a list of parameters for an input validation techniques (default: list(cv.k = 10, boot.n = 100))

prob.threshold

a numeric for probability threshold (default: 0.5)

repeats

a numeric for number of repetitions (default: 1)

n.cores

a numeric for number of cores (default: 2)