sgs (version 0.3.5)

predict.sgs: Predict using one of the following object types: "sgs", "sgs_cv", "gslope", "gslope_cv".

Description

Performs prediction from one of the following fits: 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 sgs
predict(object, x, ...)

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

object

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

x

Input data to use for prediction.

...

further arguments passed to stats 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(), plot.sgs(), print.sgs(), scaled_sgs()

Other gSLOPE-methods: coef.sgs(), fit_goscar(), fit_goscar_cv(), fit_gslope(), fit_gslope_cv(), plot.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_predictions = predict(model, x = data$X)