predict.cv.SplitGLM {SplitGLM}

Predictions for cv.SplitGLM Object

Description

predict.cv.SplitGLM returns the predictions for a SplitGLM object.

Usage

## S3 method for class 'cv.SplitGLM'
predict(object, newx, group_index = NULL, type = c("prob", "class")[1], ...)

Arguments

object

An object of class cv.SplitGLM.

newx

New data for predictions.

group_index

The group for which to return the coefficients. Default is the ensemble.

type

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

...

Additional arguments for compatibility.

Value

The predictions for the cv.SplitGLM object.

Author(s)

Anthony-Alexander Christidis, [e-mail]

See Also

cv.SplitGLM

Examples

# 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)
mean(y.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)
mean(y.test)

# SplitGLM - CV (Multiple Groups)
split.out <- cv.SplitGLM(x.train, y.train,
                         glm_type="Logistic",
                         G=10, include_intercept=TRUE,
                         alpha_s=3/4, alpha_d=1,
                         n_lambda_sparsity=50, n_lambda_diversity=50,
                         tolerance=1e-3, max_iter=1e3,
                         n_folds=5,
                         active_set=FALSE,
                         n_threads=1)
split.coef <- coef(split.out)
# Predictions
split.prob <- predict(split.out, newx=x.test, type="prob", group_index=NULL)
split.class <- predict(split.out, newx=x.test, type="class", group_index=NULL)
plot(prob.test, split.prob, pch=20)
abline(h=0.5,v=0.5)
mean((prob.test-split.prob)^2)
mean(abs(y.test-split.class))