coef.sgs: Extracts coefficients for one of the following object types:...

In sgs: Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control

Extracts coefficients for one of the following object types: "sgs", "sgs_cv", "gslope", "gslope_cv".

Description

Print the coefficients using model fitted with one of the following functions: fit_sgs(), fit_sgs_cv(), fit_gslope(), fit_gslope_cv(), fit_sgo(), fit_sgo_cv(), fit_goscar(), fit_goscar_cv(). The predictions are calculated for each "lambda" value in the path.

Usage

## S3 method for class 'sgs'
coef(object, ...)

Arguments

object	Object of one of the following classes: "sgs", "sgs_cv", "gslope", "gslope_cv".
...	further arguments passed to stats function.

Value

The fitted coefficients

See Also

fit_sgs(), fit_sgs_cv(), fit_gslope(), fit_gslope_cv()

Other SGS-methods: as_sgs(), fit_sgo(), fit_sgo_cv(), fit_sgs(), fit_sgs_cv(), plot.sgs(), predict.sgs(), print.sgs(), scaled_sgs()

Other gSLOPE-methods: fit_goscar(), fit_goscar_cv(), fit_gslope(), fit_gslope_cv(), plot.sgs(), predict.sgs(), print.sgs()

Examples

# specify a grouping structure
groups = c(1,1,1,2,2,3,3,3,4,4)
# generate data
data =  gen_toy_data(p=10, n=5, groups = groups, seed_id=3,group_sparsity=1)
# run SGS 
model = fit_sgs(X = data$X, y = data$y, groups = groups, type="linear", lambda = 1, alpha=0.95, 
vFDR=0.1, gFDR=0.1, standardise = "l2", intercept = TRUE, verbose=FALSE)
# use predict function
model_coef = coef(model)

sgs documentation built on Nov. 5, 2025, 6:24 p.m.