CPGLIB (version 1.1.2)

predict.ProxGrad: Predictions for ProxGrad Object

Description

predict.ProxGrad returns the predictions for a ProxGrad object.

Usage

# S3 method for ProxGrad
predict(object, newx, type = c("prob", "class")[1], ...)

Value

The predictions for the ProxGrad object.

Arguments

object

An object of class ProxGrad

newx

New data for predictions.

type

The type of predictions for binary response. Options are "prob" (default) and "class".

...

Additional arguments for compatibility.

Author

Anthony-Alexander Christidis,

See Also

ProxGrad

Examples

# \donttest{
# Data simulation
set.seed(1)
n <- 50
N <- 2000
p <- 1000
beta.active <- c(abs(runif(p, 0, 1/2))*(-1)^rbinom(p, 1, 0.3))
# Parameters
p.active <- 100
beta <- c(beta.active[1:p.active], rep(0, p-p.active))
Sigma <- matrix(0, p, p)
Sigma[1:p.active, 1:p.active] <- 0.5
diag(Sigma) <- 1

# Train data
x.train <- mvnfast::rmvn(n, mu = rep(0, p), sigma = Sigma) 
prob.train <- exp(x.train %*% beta)/
              (1+exp(x.train %*% beta))
y.train <- rbinom(n, 1, prob.train)
# Test data
x.test <- mvnfast::rmvn(N, mu = rep(0, p), sigma = Sigma)
prob.test <- exp(x.test %*% beta)/
             (1+exp(x.test %*% beta))
y.test <- rbinom(N, 1, prob.test)

# ProxGrad - Single Group
proxgrad.out <- ProxGrad(x.train, y.train,
                         glm_type = "Logistic",
                         include_intercept = TRUE,
                         alpha_s = 3/4,
                         lambda_sparsity = 0.01, 
                         tolerance = 1e-5, max_iter = 1e5)

# Predictions
proxgrad.prob <- predict(proxgrad.out, newx = x.test, type = "prob")
proxgrad.class <- predict(proxgrad.out, newx = x.test, type = "class")
plot(prob.test, proxgrad.prob, pch = 20)
abline(h = 0.5,v = 0.5)
mean((prob.test-proxgrad.prob)^2)
mean(abs(y.test-proxgrad.class))

# }