Make predictions from a sparsegl object.

Similar to other predict methods, this function produces fitted values and class labels from a fitted sparsegl object.

Usage

# S3 method for sparsegl
predict(
  object,
  newx,
  s = NULL,
  type = c("link", "response", "coefficients", "nonzero", "class"),
  ...
)

Arguments

object

Fitted sparsegl() model object.

newx

Matrix of new values for x at which predictions are to be made. Must be a matrix. This argument is mandatory.

s

Value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create the model.

type

Type of prediction required. Type "link" gives the linear predictors for "binomial"; for "gaussian" models it gives the fitted values. Type "response" gives predictions on the scale of the response (for example, fitted probabilities for "binomial"); for "gaussian" type "response" is equivalent to type "link". Type "coefficients" computes the coefficients at the requested values for s. Type "class" applies only to "binomial" models, and produces the class label corresponding to the maximum probability. Type "nonzero" returns a list of the indices of the nonzero coefficients for each value of s.

...

Not used.

Value

The object returned depends on type.

Details

s is the new vector of lambda values at which predictions are requested. If s is not in the lambda sequence used for fitting the model, the coef function will use linear interpolation to make predictions. The new values are interpolated using a fraction of coefficients from both left and right lambda indices.

See also

sparsegl(), coef.sparsegl().

Examples

n <- 100
p <- 20
X <- matrix(rnorm(n * p), nrow = n)
eps <- rnorm(n)
beta_star <- c(rep(5, 5), c(5, -5, 2, 0, 0), rep(-5, 5), rep(0, (p - 15)))
y <- X %*% beta_star + eps
groups <- rep(1:(p / 5), each = 5)
fit1 <- sparsegl(X, y, group = groups)
predict(fit1, newx = X[10, ], s = fit1$lambda[3:5])
#>              s1        s2       s3
#> [1,] -0.5347443 0.5858798 1.588274