predict.sparseSVM: Model predictions based on "sparseSVM" object.

In CY-dev/sparseSVM: Solution Paths of Sparse High-dimensional Support Vector Machine with Lasso or Elastic-Net Regularization

Description

This function returns fitted values, coefficients and more from a fitted "sparseSVM" object.

Usage

## S3 method for class 'sparseSVM'
predict(object, X, lambda, type = c("class","coefficients","nvars"), 
    exact = FALSE, ...)
## S3 method for class 'sparseSVM'
coef(object, lambda, exact = FALSE, ...)

Arguments

object	Fitted "sparseSVM" model object.
X	Matrix of values at which predictions are to be made. Used only for type = "class".
lambda	Values of the regularization parameter lambda at which predictions are requested. Default is the entire sequence used to create the model.
type	Type of prediction. "class" returns the class labels; "coefficients" returns the coefficients; "nvars" returns the number of nonzero coefficients at each value of lambda.
exact	If exact=FALSE (default), then the function uses linear interpolation to make predictions for values of lambda that do not coincide with those used to fit the model. If exact=TRUE, and predictions are requested at values of lambda not included in the original fit, the model is refit on a lambda sequence consisting object$lambda and the new ones before predictions are made.
...	Not used. Other arguments to predict.

Value

The object returned depends on type.

Author(s)

Congrui Yi and Yaohui Zeng
Maintainer: Congrui Yi <[e-mail]>

See Also

sparseSVM

Examples

X = matrix(rnorm(1000*100), 1000, 100)
b = 3
w = 5*rnorm(10)
eps = rnorm(1000)
y = sign(b + drop(X[,1:10] %*% w + eps))

fit = sparseSVM(X, y)
predict(fit, X[1:5,], lambda = c(0.05, 0.03))
predict(fit, X[1:5,], lambda = 0.05, exact = TRUE)
predict(fit, type = "nvars")
coef(fit, lambda = 0.05)

CY-dev/sparseSVM documentation built on Aug. 19, 2019, 2:39 a.m.