predict.srlars: Predictions for srlars Object

In srlars: Fast and Scalable Cellwise-Robust Ensemble

Predictions for srlars Object

Description

predict.srlars returns the predictions for a srlars object.

Usage

## S3 method for class 'srlars'
predict(object, newx, model_index = NULL, dynamic = TRUE, ...)

Arguments

object	An object of class srlars.
newx	New data matrix for predictions.
model_index	Indices of the sub-models to include in the ensemble. Default is NULL (all models).
dynamic	Logical. If TRUE, and the model was trained robustly, the new data newx is cleaned using DDCpredict before prediction. This ensures consistency with the robust training phase. Default is TRUE.
...	Additional arguments for compatibility.

Value

A numeric vector of predictions.

Author(s)

Anthony-Alexander Christidis, [e-mail]

See Also

srlars

Examples

# Required libraries
library(mvnfast)
library(cellWise)
library(robustbase)

# Simulation parameters
n <- 50
p <- 100
rho.within <- 0.8
rho.between <- 0.2
p.active <- 20
group.size <- 5
snr <- 3
contamination.prop <- 0.1

# Setting the seed
set.seed(0)

# Block correlation structure
sigma.mat <- matrix(0, p, p)
sigma.mat[1:p.active, 1:p.active] <- rho.between
for(group in 0:(p.active/group.size - 1))
  sigma.mat[(group*group.size+1):(group*group.size+group.size),
  (group*group.size+1):(group*group.size+group.size)] <- rho.within
diag(sigma.mat) <- 1

# Simulation of beta vector
true.beta <- c(runif(p.active, 0, 5)*(-1)^rbinom(p.active, 1, 0.7), rep(0, p - p.active))

# Setting the SD of the variance
sigma <- as.numeric(sqrt(t(true.beta) %*% sigma.mat %*% true.beta)/sqrt(snr))

# Simulation of uncontaminated data
x <- mvnfast::rmvn(n, mu = rep(0, p), sigma = sigma.mat)
colnames(x) <- paste0("V", 1:p)
y <- x %*% true.beta + rnorm(n, 0, sigma)

# Cellwise contamination
contamination_indices <- sample(1:(n * p), round(n * p * contamination.prop))
x_train <- x
x_train[contamination_indices] <- runif(length(contamination_indices), -10, 10)

# FSCRE Ensemble model
ensemble_fit <- srlars(x_train, y,
                       n_models = 5,
                       tolerance = 1e-4,
                       x_preprocess = "ddc",
                       y_preprocess = "wrap",
                       cor_estimator = "wrap",
                       cv_preprocess = "global",
                       cv_fit = "ls",
                       cv_loss = "huber",
                       compute_coef = TRUE)

# Generate Test Data
x_test <- mvnfast::rmvn(50, mu = rep(0, p), sigma = sigma.mat)
colnames(x_test) <- paste0("V", 1:p)
y_test <- x_test %*% true.beta + rnorm(50, 0, sigma)

# Predict on Test Data
preds <- predict(ensemble_fit, x_test)

# Calculate MSPE
mspe <- mean((y_test - preds)^2)
print(paste("MSPE:", mspe))

srlars documentation built on Sept. 23, 2026, 5:10 p.m.