Investigate variable relations of a specific variable with mean adjusted agreement

This function uses the mean adjusted agreement to select variables that are related to a defined variable using a threshold T. The parameter t is used to calculate T: t=1 means that every variable with higher probability than "by chance" is identified as "important". t=2 means the probability has to be twice, etc. Based on the threshold a vector is created containing the related variables.

Usage

MeanAdjustedAgreement(
  RFS,
  t = 5,
  variables = RFS$ranger$forest$independent.variable.names,
  candidates = RFS$ranger$forest$independent.variable.names,
  related = TRUE,
  num.threads = 1
)

Arguments

RFS

A RandomForestSurrogates() object.

t

(Default: 5) Used to calculate threshold.

variables

Vector of variable names for which related variables should be searched. (Default: All variables used to create the random forest.)

candidates

Vector of variable names that are candidates to be related to the variables. (Default: All variables used to create the random forest.)

related

(Default: TRUE) Whether related variables should be identified.

num.threads

(Default: 1) Number of threads used for determination of relations.

Value

A MeanAdjustedAgreement list object:

• RFS: The original RandomForestSurrogates() object.

• relations: Matrix with mean adjusted agreement values

  • Rows: variables.

  • Columns: candidates.

• threshold: the threshold used to select related variables.

• related: A list of vectors for each variable containing related candidates. Only present if related = TRUE (Default).

Examples

# \donttest{
data("SMD_example_data")
rfs <- RandomForestSurrogates(
  x = SMD_example_data[, -1],
  y = SMD_example_data[, 1],
  s = 10,
  seed = 42,
  num.trees = 10,
  num.threads = 1
)
maa <- MeanAdjustedAgreement(
  rfs,
  variables = c("X7", "X1"),
  candidates = colnames(SMD_example_data)[2:101],
  t = 5,
  num.threads = 1
)
# }