correl_filter: Correlation filter

In nestedcv: Nested Cross-Validation with 'glmnet' and 'caret'

Correlation filter

Description

Filter using correlation (Pearson or Spearman) for ranking variables.

Usage

correl_filter(
  y,
  x,
  method = "pearson",
  force_vars = NULL,
  nfilter = NULL,
  p_cutoff = 0.05,
  rsq_cutoff = NULL,
  type = c("index", "names", "full"),
  keep_factors = TRUE,
  ...
)

Arguments

y	Response vector
x	Matrix or dataframe of predictors
method	Type of correlation, either "pearson" or "spearman".
force_vars	Vector of column names within x which are always retained in the model (i.e. not filtered). Default NULL means all predictors will be passed to filterFUN.
nfilter	Number of predictors to return. If NULL all predictors with p values < p_cutoff are returned.
p_cutoff	p value cut-off
rsq_cutoff	r^2 cutoff for removing predictors due to collinearity. Default NULL means no collinearity filtering. Predictors are ranked based on correlation with the response vector y. If 2 or more predictors are collinear, the first ranked predictor is retained, while the other collinear predictors are removed. See collinear().
type	Type of vector returned. Default "index" returns indices, "names" returns predictor names, "full" returns a matrix of p-values.
keep_factors	Logical affecting factors with 3 or more levels. Dataframes are coerced to a matrix using data.matrix. Binary factors are converted to numeric values 0/1 and analysed as such. If keep_factors is TRUE (the default), factors with 3 or more levels are not filtered and are retained. If keep_factors is FALSE, they are removed.
...	Further arguments passed to correls

Value

Integer vector of indices of filtered parameters (type = "index") or character vector of names (type = "names") of filtered parameters. If type is "full" full output from correls is returned.

nestedcv documentation built on Oct. 26, 2023, 5:08 p.m.