FSboost_normal: Normal non-kernel based Forward Stepwise boosted regression...
In kkdey/fineboostR:

FSboost_normal

R Documentation

Normal non-kernel based Forward Stepwise boosted regression model.

Description

This function uses a non-kernel-based FS-boost framework to find causal effect sizes in regression Y = Xb + e where b is a sparse vector of coefficients. This is more a boosting method to find sparse solutions to b and not a fine-mapping driven method.

Usage

FSboost_normal(X, Y, M = 1000, Lmax = 5, LD = NULL, step = 0.05,
  stop_thresh = 1e-04, na.rm = FALSE, intercept = TRUE,
  standardize = TRUE, coverage = 0.95, clus_thresh = 0.1,
  nmf_try = 5, verbose = TRUE)

Arguments

`X`	The design matrix X (N times P) with samples/individuals along the rows and putatively correlated ordered features (SNPs) along the columns.
`Y`	The response vector of length N
`M`	The maximum number of boosting iterations to run. Default is 1000.
`Lmax`	The maximum number of local signal clusters fitted.
`LD`	The external LD matrix for the P features of interest. Defaults to NULL, in which case, in-sample LD is used.
`step`	The stepsize used in boosting iterations. Default set to 0.05.
`stop_thresh`	The stopping threshold (small number) for the objective function, when attained, the boosting iterations will stop automatically. Default is 0.1.
`na.rm`	Drop missing samples in y from both y and X inputs. Default set to FALSE.
`intercept`	Boolean; if there is an intercept in the model to fit. Defaults to TRUE.
`standardize`	Boolean; if the columns of X need to be standardized. Defaults to TRUE.
`coverage`	A number between 0 and 1 (close to 1) specifying the coverage of the estimated signal clusters. Default set to 0.95.
`clus_thresh`	A number between 0 and 1 (close to 0) that is used to filter out local signal clusters with depleted number of boosting iterations and high level of uniformity of signal. Default is set to 0.1.
`nmf_try`	The number of NMF initiializations to fix the confidence sets. Default is set to 5.
`verbose`	If `verbose = TRUE`, information about the objective and progress at each iteration of the kerne-based boosting procedure is returned.
`min_abs_corr`	Minimum of absolute value of correlation allowed in a credible set. The default, 0.5, corresponds to squared correlation of 0.25, which is a commonly used threshold for genotype data in genetics studies.