predict.sgl: Predict using one of the following object types: '"sgl"',...

In dfr: Dual Feature Reduction for SGL

Predict using one of the following object types: "sgl", "sgl_cv".

Description

Performs prediction from one of the following fits: dfr_sgl(), dfr_sgl.cv(), dfr_adap_sgl(), dfr_adap_sgl.cv(). The predictions are calculated for each "lambda" value in the path.

Usage

## S3 method for class 'sgl'
predict(object, x, ...)

Arguments

object	Object of one of the following classes: "sgl", "sgl_cv".
x	Input data to use for prediction.
...	further arguments passed to stats function.

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 "sgl" or "sgl_cv" object.

See Also

dfr_sgl(), dfr_sgl.cv(), dfr_adap_sgl(), dfr_adap_sgl.cv()

Other SGL-methods: dfr_adap_sgl(), dfr_adap_sgl.cv(), dfr_sgl(), dfr_sgl.cv(), plot.sgl(), print.sgl()

Examples

# specify a grouping structure
groups = c(1,1,1,2,2,3,3,3,4,4)
# generate data
data = sgs::gen_toy_data(p=10, n=5, groups = groups, seed_id=3,group_sparsity=1)
# run DFR-SGL 
model = dfr_sgl(X = data$X, y = data$y, groups = groups, type="linear", lambda = 1, alpha=0.95, 
standardise = "l2", intercept = TRUE, verbose=FALSE)
# use predict function
model_predictions = predict(model, x = data$X)

dfr documentation built on April 4, 2025, 4:43 a.m.