sgs (version 0.3.6)

plot.sgs: Plot models of the following object types: "sgs", "sgs_cv", "gslope", "gslope_cv".

Description

Plots the pathwise solution of a cross-validation fit, from a call to one of the following: fit_sgs(), fit_sgs_cv(), fit_gslope(), fit_gslope_cv(), fit_sgo(), fit_sgo_cv(), fit_goscar(), fit_goscar_cv().

Usage

# S3 method for sgs
plot(x, how_many = 10, ...)

Value

A list containing:

response

The predicted response. In the logistic case, this represents the predicted class probabilities.

class

The predicted class assignments. Only returned if type = "logistic" in the model object.

Arguments

x

Object of one of the following classes: "sgs", "sgs_cv", "gslope", "gslope_cv".

how_many

Defines how many predictors to plot. Plots the predictors in decreasing order of largest absolute value.

...

further arguments passed to base function.

See Also

fit_sgs(), fit_sgs_cv(), fit_gslope(), fit_gslope_cv(), fit_sgo(), fit_sgo_cv(), fit_goscar(), fit_goscar_cv()

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

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

Examples

# specify a grouping structure
groups = c(1,1,2,2,3)
# generate data
data =  gen_toy_data(p=5, n=4, groups = groups, seed_id=3,signal_mean=20,group_sparsity=1)
# run SGS 
model = fit_sgs(X = data$X, y = data$y, groups=groups, type = "linear", 
path_length = 20, alpha = 0.95, vFDR = 0.1, gFDR = 0.1, 
min_frac = 0.05, standardise="l2",intercept=TRUE,verbose=FALSE)
plot(model, how_many = 10)