srlars (version 2.0.0)

predict.srlars: Predictions for srlars Object

Description

predict.srlars returns the predictions for a srlars object.

Usage

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

Value

A numeric vector of predictions.

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.

Author

Anthony-Alexander Christidis,

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)
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 = 0.01,
                       robust = TRUE,
                       compute_coef = TRUE)

# Simulation of test data
m <- 50
x_test <- mvnfast::rmvn(m, mu = rep(0, p), sigma = sigma.mat)
y_test <- x_test %*% true.beta + rnorm(m, 0, sigma)

# Prediction of test samples
# Default: Averaged prediction from all models
# Dynamic imputation is used by default if the model is robust
ensemble_preds <- predict(ensemble_fit, newx = x_test)

# Evaluate MSPE
mspe <- mean((y_test - ensemble_preds)^2) / sigma^2
print(paste("MSPE:", mspe))