CheckInputGraphical: Checking input parameters (graphical model)

In sharp: Stability-enHanced Approaches using Resampling Procedures

Checking input parameters (graphical 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

CheckInputGraphical(
  xdata,
  pk = NULL,
  Lambda = NULL,
  lambda_other_blocks = 0.1,
  pi_list = seq(0.6, 0.9, by = 0.01),
  K = 100,
  tau = 0.5,
  seed = 1,
  n_cat = 3,
  implementation = PenalisedGraphical,
  start = "cold",
  scale = TRUE,
  resampling = "subsampling",
  PFER_method = "MB",
  PFER_thr = Inf,
  FDP_thr = Inf,
  Lambda_cardinal = 50,
  lambda_max = NULL,
  lambda_path_factor = 1e-04,
  max_density = 0.3,
  verbose = TRUE
)

Arguments

xdata	data matrix with observations as rows and variables as columns. For multi-block stability selection, the variables in data have to be ordered by group.
pk	optional vector encoding the grouping structure. Only used for multi-block stability selection where pk indicates the number of variables in each group. If pk=NULL, single-block stability selection is performed.
Lambda	matrix of parameters controlling the level of sparsity in the underlying feature selection algorithm specified in implementation. If Lambda=NULL and implementation=PenalisedGraphical, LambdaGridGraphical is used to define a relevant grid. Lambda can be provided as a vector or a matrix with length(pk) columns.
lambda_other_blocks	optional vector of parameters controlling the level of sparsity in neighbour blocks for the multi-block procedure. To use jointly a specific set of parameters for each block, lambda_other_blocks must be set to NULL (not recommended). Only used for multi-block stability selection, i.e. if length(pk)>1.
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.
implementation	function to use for graphical modelling. If implementation=PenalisedGraphical, the algorithm implemented in glassoFast is used for regularised estimation of a conditional independence graph. Alternatively, a user-defined function can be provided.
start	character string indicating if the algorithm should be initialised at the estimated (inverse) covariance with previous penalty parameters (start="warm") or not (start="cold"). Using start="warm" can speed-up the computations, but could lead to convergence issues (in particular with small Lambda_cardinal). Only used for implementation=PenalisedGraphical (see argument "start" in glassoFast).
scale	logical indicating if the correlation (scale=TRUE) or covariance (scale=FALSE) matrix should be used as input of glassoFast if implementation=PenalisedGraphical. Otherwise, this argument must be used in the function provided in implementation.
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.
lambda_max	optional maximum value for the grid in penalty parameters. If lambda_max=NULL, the maximum value is set to the maximum covariance in absolute value. Only used if implementation=PenalisedGraphical and Lambda=NULL.
lambda_path_factor	multiplicative factor used to define the minimum value in the grid.
max_density	threshold on the density. The grid is defined such that the density of the estimated graph does not exceed max_density.
verbose	logical indicating if a loading bar and messages should be printed.

sharp documentation built on April 11, 2025, 5:44 p.m.