predict.sgl {dfr} | R Documentation |
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: |
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 |
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)
[Package dfr version 0.1.5 Index]