PSGD (version 1.0.6)

predict.PSGD: Predictions for PSGD Object

Description

predict.PSGD returns the predictions for a PSGD object.

Usage

# S3 method for PSGD
predict(object, newx, group_index = NULL, ...)

Value

The predictions for the PSGD object.

Arguments

object

An object of class PSGD

newx

New data for predictions.

group_index

Groups included in the ensemble. Default setting includes all the groups.

...

Additional arguments for compatibility.

Author

Anthony-Alexander Christidis,

See Also

PSGD

Examples

# Required Libraries
library(mvnfast)

# Setting the parameters
p <- 100
n <- 40
n.test <- 1000
sparsity <- 0.2
rho <- 0.5
SNR <- 3

# Generating the coefficient
p.active <- floor(p*sparsity)
a <- 4*log(n)/sqrt(n)
neg.prob <- 0.2
nonzero.betas <- (-1)^(rbinom(p.active, 1, neg.prob))*(a + abs(rnorm(p.active)))

# Correlation structure
Sigma <- matrix(0, p, p)
Sigma[1:p.active, 1:p.active] <- rho
diag(Sigma) <- 1
true.beta <- c(nonzero.betas, rep(0 , p - p.active))

# Computing the noise parameter for target SNR
sigma.epsilon <- as.numeric(sqrt((t(true.beta) %*% Sigma %*% true.beta)/SNR))

# Simulate some data
set.seed(1)
x.train <- mvnfast::rmvn(n, mu=rep(0,p), sigma=Sigma)
y.train <- 1 + x.train %*% true.beta + rnorm(n=n, mean=0, sd=sigma.epsilon)
x.test <- mvnfast::rmvn(n.test, mu=rep(0,p), sigma=Sigma)
y.test <- 1 + x.test %*% true.beta + rnorm(n.test, sd=sigma.epsilon)

# PSGD Ensemble
output <- PSGD(x = x.train, y = y.train, n_models = 5,
               model_type = c("Linear", "Logistic")[1], include_intercept = TRUE, 
               split = 3, size = 10, 
               max_iter = 20,
               cycling_iter = 0)
psgd.coef <- coef(output, group_index = 1:output$n_models)
psgd.predictions <- predict(output, newx = x.test, group_index = 1:output$n_models)
mean((y.test - psgd.predictions)^2)/sigma.epsilon^2