predict.cv.rbridge: Predict from a fitted cv.rbridge object

In rbridge: Restricted Bridge Estimation

Predict from a fitted cv.rbridge object

Description

Compute response predictions, extract coefficients, or identify nonzero predictors from a fitted cv.rbridge object.

Usage

## S3 method for class 'cv.rbridge'
predict(
  object,
  newx,
  s = c("lambda.min", "lambda.1se"),
  type = c("response", "nonzero", "coefficients"),
  ...
)

Arguments

object	Fitted object returned by cv.rbridge, with p predictors in its training design.
newx	Numeric matrix with one row per new observation and the same p columns, in the same order, as the training design. A numeric vector of length p represents one observation. Values are supplied on the original predictor scale. Required only for type = "response".
s	Penalty selection, using numeric fitted values or a character name. Numeric values must match object$lambda exactly; no interpolation is used. The character selectors are: "lambda.min" Select the penalty with minimum CV error. "lambda.1se" Select using the historical CV error bars. The default is lambda.min.
type	Output to return: "response" for fitted response values, "coefficients" for intercept and slopes, or "nonzero" for predictor indices over the entire fitted path.
...	Additional arguments accepted for compatibility.

Details

Prediction uses the fitted intercept and original-scale slopes. Columns of newx are matched by position, not reordered by their names. For type = "nonzero", all fitted penalties are inspected and s does not restrict the returned indices. The defaults differ between coef() and predict(): coefficient extraction uses lambda.1se, whereas response prediction uses lambda.min. Specify the same s in both calls when comparing coefficients and their predictions. Returned slopes are on the original predictor scale; the linear restrictions concern standardized slopes. See rbridge for converting scales before checking restriction residuals.

Value

For type = "response", a numeric vector for one selected penalty or a matrix with one row per new observation and one column per selected penalty. For type = "coefficients", a numeric matrix with p + 1 rows, including the intercept, even for one penalty. For type = "nonzero", a pathwise collection of 1-based predictor indices excluding the intercept. Its historical container can simplify depending on the support pattern and number of predictors.

Author(s)

Bahadir Yuzbasi
Maintainer: Bahadir Yuzbasi [e-mail]

See Also

coef.cv.rbridge, cv.rbridge

Examples

# Reproducible synthetic demonstration: 100 observations, eight predictors.
set.seed(2019)
beta <- c(3, 1.5, 0, 0, 2, 0, 0, 0)
p <- length(beta)
beta <- matrix(beta, nrow = p, ncol = 1)
n <- 100
X <- matrix(rnorm(n * p), nrow = n, ncol = p)
y <- as.numeric(X %*% beta + rnorm(n))
# A user-specified sum of standardized slopes: b[1] + b[2] + b[5] = 6.5.
# This target is illustrative; it is not asserted to equal the generating sum.
R1.mat <- matrix(c(1, 1, 0, 0, 1, 0, 0, 0), nrow = 1)
r1.vec <- matrix(6.5, nrow = 1, ncol = 1)

# Five penalties keep this help example fast; the default is 100.
model1 <- cv.rbridge(X, y, q = 1, R = R1.mat, r = r1.vec, nlambda = 5,
                     nfolds = 3, num_threads = 1)
# Predict five observations at one explicitly selected penalty.
predict(model1, newx = X[1:5, , drop = FALSE], s = "lambda.min")
# Extract coefficients using the complete type name.
predict(model1, s = "lambda.min", type = "coefficients")
# Predictor indices for every fitted penalty; the intercept is excluded.
predict(model1, type = "nonzero")

rbridge documentation built on Oct. 13, 2026, 9:08 a.m.