View source: R/Prediction_Functions.R
predict.SplitGLM | R Documentation |
predict.SplitGLM
returns the predictions for a SplitGLM object.
## S3 method for class 'SplitGLM'
predict(object, newx, group_index = NULL, type = c("prob", "class")[1], ...)
object |
An object of class 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. |
The predictions for the SplitGLM object.
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
SplitGLM
# 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 <- SplitGLM(x.train, y.train,
glm_type="Logistic",
G=10, include_intercept=TRUE,
alpha_s=3/4, alpha_d=1,
lambda_sparsity=1, lambda_diversity=1,
tolerance=1e-3, max_iter=1e3,
active_set=FALSE)
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))
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