do_feature_selection: Perform feature selection based on different methods
In cgplab/RadAR: Radiomic Analysis with R

Description Usage Arguments Value Author(s)

This function implements different methods to perform feature selection of radiomic datasets.

do_feature_selection(
  rdr = NULL,
  n_features = NULL,
  select_by = NULL,
  method = "hcl",
  surv_obj = NULL,
  which_data = "scaled",
  corr_measure = "pearson",
  min_features_per_group = 5,
  thr_pca_cum_prop = 0.8,
  response = NULL,
  lambda = "min",
  alpha = NULL
)

`rdr`	A RadAR object (class `SummarizedExperiment`).
`n_features`	(numeric) Number of features to be selected. Required.
`select_by`	(character) Which criteria use to select informative radiomic features within clusters of similar (i.e., redundant) features. It can be one of the following: "variability", "random", "concordance".
`method`	(character) Which method use to identify redundant features. It can be one of the following: "mRMR" (minimum-redundancy-maximum-relevance),"hcl" (hierarchical clustering of correlation matrix), "pca" (K-means applied to Principal Component Analysis), "glmnet-cox" (generalized linear model via penalized maximum likelihood (glmnet) fitting cox regression model), "glmnet-binonial" (glmnet fitting binomial regression model), Using mRMR, this function works as a wrapper to `mRMR` package. Using glmnet-*, this function works as a wrapper to `glmnet` package.
`surv_obj`	An object of class `Surv`. Required if select_by is "concordance".
`which_data`	(character) Which data use to compute concordance index. It can be one of the following: "normal", "scaled", "normalized".
`corr_measure`	(character) Which method use to calculate correlation. It can be one of the following: "pearson", "kendall", "spearman".
`min_features_per_group`	(numeric) Minimum number of features for each cluster.
`thr_pca_cum_prop`	(numeric) Threshold to select number of components based on cumulative proportion of explained variance criterion.
`response`	(numeric) A response variable, required if any of mRMR or glmnet-binomial or methods are used.
`lambda`	(character) In glmnet, it controls the overall strength of the penalty. Possible values are "min" or "1se" (1 standard deviation). For more details see `glmnet`
`alpha`	(numeric) In glmnet, it controls elastic-net penalty. Typical values are 0 (ridge) or 1 (lasso). For more details see `glmnet`.