predict.cv.SplitGLM: Predictions for cv.SplitGLM Object

In SplitGLM: Split Generalized Linear Models

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))

SplitGLM documentation built on Nov. 22, 2022, 5:06 p.m.