var.relations.corr: Investigate variable relations of a specific variable with...

In StephanSeifert/SurrogateMinimalDepth: Surrogate minimal depth variable importance

Investigate variable relations of a specific variable with corrected mean adjusted agreement.

Description

This function corrects the mean adjusted agreement by a permutation approach. Subsequently p-values are determined andrelated variables are selected.

Usage

var.relations.corr(
  x = NULL,
  y = NULL,
  ntree = 500,
  type = "regression",
  s = NULL,
  mtry = NULL,
  min.node.size = 1,
  num.threads = NULL,
  status = NULL,
  save.ranger = FALSE,
  create.forest = TRUE,
  forest = NULL,
  save.memory = FALSE,
  case.weights = NULL,
  variables,
  candidates,
  p.t = 0.01,
  select.rel = TRUE,
  method = "janitza",
  num.threads.rel = NULL
)

Arguments

x	matrix or data.frame of predictor variables with variables in columns and samples in rows (Note: missing values are not allowed)
y	vector with values of phenotype variable (Note: will be converted to factor if classification mode is used). For survival forests this is the time variable.
ntree	number of trees. Default is 500.
type	mode of prediction ("regression" or "classification"). Default is regression.
s	predefined number of surrogate splits (it may happen that the actual number of surrogate splits differs in individual nodes). Default is 1 % of no. of variables.
mtry	number of variables to possibly split at in each node. Default is no. of variables^(3/4) ("^3/4") as recommended by (Ishwaran 2011). Also possible is "sqrt" and "0.5" to use the square root or half of the no. of variables.
min.node.size	minimal node size. Default is 1.
num.threads	number of threads used for parallel execution. Default is number of CPUs available.
status	status variable, only applicable to survival data. Use 1 for event and 0 for censoring.
save.ranger	set TRUE if ranger object should be saved. Default is that ranger object is not saved (FALSE).
create.forest	set FALSE if you want to analyze an existing forest. Default is TRUE.
forest	the random forest that should be analyzed if create.forest is set to FALSE. (x and y still have to be given to obtain variable names)
save.memory	Use memory saving (but slower) splitting mode. No effect for survival and GWAS data. Warning: This option slows down the tree growing, use only if you encounter memory problems. (This parameter is transfered to ranger)
case.weights	Weights for sampling of training observations. Observations with larger weights will be selected with higher probability in the bootstrap (or subsampled) samples for the trees.
variables	variable names (string) for which related variables should be searched for (has to be contained in allvariables)
candidates	vector of variable names (strings) that are candidates to be related to the variables (has to be contained in allvariables)
p.t	p.value threshold for selection of related variables. Default is 0.01.
select.rel	set False if only relations should be calculated and no related variables should be selected.
method	Method to compute p-values. Use "janitza" for the method by Janitza et al. (2016) or "permutation" to utilize importance values of permuted variables.
num.threads.rel	number of threads used for determination of relations. Default is number of CPUs available. (this process can be memory-intensive and it can be preferable to reduce this)

Value

a list containing:

• variables: the variables to which relations are investigated.

• surr.res: a matrix with corrected mean adjusted agreement values with variables in rows and candidates in columns.

• p.rel: a list with the obtained p-values for the relation analysis of each variable.

• var.rel: a list with vectors of related variables for each variable.

• ranger: ranger objects.

• method: Method to compute p-values: "janitza" or "permutation".

• p.t: p.value threshold for selection of related variables

Examples

# read data
data("SMD_example_data")
x = SMD_example_data[,2:ncol(SMD_example_data)]
y = SMD_example_data[,1]

# calculate variable relations
set.seed(42)
res = var.relations.corr(x = x, y = y, s = 10, ntree = 100, variables = c("X1","X7"), candidates = colnames(x)[1:100], t = 5)
res$var.rel[[1]]

StephanSeifert/SurrogateMinimalDepth documentation built on Aug. 7, 2023, 1:59 a.m.