mlCutpoint: Machine-learning Approach for Cutpoint Estimation

In genomaths/MethylIT: Methylation Analysis Based on Signal Detection

Machine-learning Approach for Cutpoint Estimation

Description

Internal function to estimate cutpoint following a machine-learning approach

Usage

mlCutpoint(
  LR,
  control.names,
  treatment.names,
  column,
  div.col,
  tv.col = NULL,
  tv.cut,
  post.cut = 0.5,
  classifier1,
  classifier2 = NULL,
  interactions = NULL,
  n.pc,
  prop = 0.6,
  center = FALSE,
  scale = FALSE,
  stat = 0L,
  cut.values = NULL,
  maxnodes = NULL,
  ntree = 400,
  nsplit = 1L,
  num.cores = 1L,
  tasks = 0L,
  ...
)

Arguments

LR, res, control.names, treatment.names, column, div.col	Same as in estimateCutPoint
column, div.col, tv.col, tv.cut, cut.values, stat	Same as in estimateCutPoint
classifier1, classifier2, n.pc, prop, post.cut	Same as in estimateCutPoint
interactions	If a logistic classifier is used. Variable interactions to consider in a logistic regression model. Any pairwise combination of the variable 'hdiv', 'TV', 'wprob', and 'pos' can be provided. For example: 'hdiv:TV', 'wprob:pos', 'wprob:TV', etc.
center	A logical value indicating whether the variables should be shifted to be zero centered.
scale	A logical value indicating whether the variables should be
maxnodes, ntree	Only for Random Forest classifier (randomForest, 'random_forest'). Maximum number maxnodes of terminal nodes trees in the forest can have. If not given, trees are grown to the maximum possible. Parameter ntree stands for the number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times.
nsplit	Only for Random Forest classifier. The Random Forest (randomForest, 'random_forest') package uses a C+Fortran implementation which only supports integer indexes, so any dataframe/data table/matrix with >2^31 elements (limit for integers) gives an error. The option nsplit is applied to train ntrees=floor(ntree/nsplit) models with rep(ntrees,nsplit) which are finally combined to obtain a forest with ntree. Each model in this would contain ntrees.
...	Additional arguments for evaluateDIMPclass function
cutp_data, num.cores, tasks	Same as in estimateCutPoint

Details

This function is called by function estimateCutPoint.

Value

Specified in function estimateCutPoint for parameter setting simple = FALSE

Author(s)

Robersy Sanchez (https://genomaths.com).

Examples

## Get a set of potential DMPS (PS)
data(PS, package = 'MethylIT')

cutp <- mlCutpoint(LR = PS,
                 column = c(hdiv = TRUE, TV = TRUE,
                            wprob = TRUE, pos = TRUE),
                 classifier1 = 'qda', n.pc = 4,
                 control.names = c('C1', 'C2', 'C3'),
                 treatment.names = c('T1', 'T2', 'T3'),
                 tv.cut = 0.68, prop = 0.6,
                 div.col = 9L)
cutp$testSetPerformance

genomaths/MethylIT documentation built on Feb. 3, 2024, 1:24 a.m.