CheckParamRegression: Checking input parameters (regression model)

Description

Checks if input parameters are valid. For invalid parameters, this function (i) stops the run and generates an error message, or (ii) sets the invalid parameter to its default value and reports it in a warning message.

Usage

CheckParamRegression(
  Lambda = NULL,
  pi_list = seq(0.6, 0.9, by = 0.01),
  K = 100,
  tau = 0.5,
  seed = 1,
  n_cat = NULL,
  family = "gaussian",
  implementation = PenalisedRegression,
  resampling = "subsampling",
  PFER_method = "MB",
  PFER_thr = Inf,
  FDP_thr = Inf,
  Lambda_cardinal = 100,
  verbose = TRUE
)

Arguments

Lambda: matrix of parameters controlling the level of sparsity in the underlying feature selection algorithm specified in implementation. If Lambda=NULL and implementation=PenalisedRegression, LambdaGridRegression is used to define a relevant grid.
pi_list: vector of thresholds in selection proportions. If n_cat=NULL or n_cat=2, these values must be >0 and <1. If n_cat=3, these values must be >0.5 and <1.
K: number of resampling iterations.
tau: subsample size. Only used if resampling="subsampling" and cpss=FALSE.
seed: value of the seed to initialise the random number generator and ensure reproducibility of the results (see set.seed).
n_cat: computation options for the stability score. Default is NULL to use the score based on a z test. Other possible values are 2 or 3 to use the score based on the negative log-likelihood.
family: type of regression model. This argument is defined as in glmnet. Possible values include "gaussian" (linear regression), "binomial" (logistic regression), "multinomial" (multinomial regression), and "cox" (survival analysis).
implementation: function to use for variable selection. Possible functions are: PenalisedRegression, SparsePLS, GroupPLS and SparseGroupPLS. Alternatively, a user-defined function can be provided.
resampling: resampling approach. Possible values are: "subsampling" for sampling without replacement of a proportion tau of the observations, or "bootstrap" for sampling with replacement generating a resampled dataset with as many observations as in the full sample. Alternatively, this argument can be a function to use for resampling. This function must use arguments named data and tau and return the IDs of observations to be included in the resampled dataset.
PFER_method: method used to compute the upper-bound of the expected number of False Positives (or Per Family Error Rate, PFER). If PFER_method="MB", the method proposed by Meinshausen and Bühlmann (2010) is used. If PFER_method="SS", the method proposed by Shah and Samworth (2013) under the assumption of unimodality is used.
PFER_thr: threshold in PFER for constrained calibration by error control. If PFER_thr=Inf and FDP_thr=Inf, unconstrained calibration is used (the default).
FDP_thr: threshold in the expected proportion of falsely selected features (or False Discovery Proportion) for constrained calibration by error control. If PFER_thr=Inf and FDP_thr=Inf, unconstrained calibration is used (the default).
Lambda_cardinal: number of values in the grid of parameters controlling the level of sparsity in the underlying algorithm. Only used if Lambda=NULL.
verbose: logical indicating if a loading bar and messages should be printed.