| Type: | Package |
| Title: | Competing Proximal Gradients Library |
| Version: | 1.1.2 |
| Date: | 2025-03-30 |
| Maintainer: | Anthony Christidis <anthony.christidis@stat.ubc.ca> |
| Description: | Functions to generate ensembles of generalized linear models using competing proximal gradients. The optimal sparsity and diversity tuning parameters are selected via an alternating grid search. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Biarch: | true |
| Imports: | Rcpp (≥ 1.0.3) |
| LinkingTo: | Rcpp, RcppArmadillo |
| RoxygenNote: | 7.1.1 |
| Suggests: | testthat, vctrs, mvnfast |
| NeedsCompilation: | yes |
| Packaged: | 2025-03-30 17:28:52 UTC; anthony |
| Author: | Anthony Christidis [aut, cre], Stefan Van Aelst [aut], Ruben Zamar [aut] |
| Repository: | CRAN |
| Date/Publication: | 2025-03-30 18:10:02 UTC |
Generalized Linear Models via Proximal Gradients
Description
ProxGrad computes the coefficients for generalized linear models using proximal gradients.
Usage
ProxGrad(
x,
y,
glm_type = c("Linear", "Logistic")[1],
include_intercept = TRUE,
alpha_s = 3/4,
lambda_sparsity,
tolerance = 1e-08,
max_iter = 1e+05
)
Arguments
x |
Design matrix. |
y |
Response vector. |
glm_type |
Description of the error distribution and link function to be used for the model. Must be one of "Linear" or "Logistic" . Default is "Linear". |
include_intercept |
Argument to determine whether there is an intercept. Default is TRUE. |
alpha_s |
Elastic net mixing parmeter. Default is 3/4. |
lambda_sparsity |
Sparsity tuning parameter value. |
tolerance |
Convergence criteria for the coefficients. Default is 1e-8. |
max_iter |
Maximum number of iterations in the algorithm. Default is 1e5. |
Value
An object of class ProxGrad.
Author(s)
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
See Also
coef.ProxGrad, predict.ProxGrad
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)
# 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))
Coefficients for CPGLIB Object
Description
coef.CPGLIB returns the coefficients for a CPGLIB object.
Usage
## S3 method for class 'CPGLIB'
coef(object, groups = NULL, ensemble_average = FALSE, ...)
Arguments
object |
An object of class CPGLIB. |
groups |
The groups in the ensemble for the coefficients. Default is all of the groups in the ensemble. |
ensemble_average |
Option to return the average of the coefficients over all the groups in the ensemble. Default is FALSE. |
... |
Additional arguments for compatibility. |
Value
The coefficients for the CPGLIB object.
Author(s)
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
See Also
Examples
# Data simulation
set.seed(1)
n <- 50
N <- 2000
p <- 300
beta.active <- c(abs(runif(p, 0, 1/2))*(-1)^rbinom(p, 1, 0.3))
# Parameters
p.active <- 150
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)
# CPGLIB - Multiple Groups
cpg.out <- cpg(x.train, y.train,
glm_type="Logistic",
G=5, include_intercept=TRUE,
alpha_s=3/4, alpha_d=1,
lambda_sparsity=0.01, lambda_diversity=1,
tolerance=1e-5, max_iter=1e5)
# Coefficients for each group
cpg.coef <- coef(cpg.out, ensemble_average = FALSE)
Coefficients for ProxGrad Object
Description
coef.ProxGrad returns the coefficients for a ProxGrad object.
Usage
## S3 method for class 'ProxGrad'
coef(object, ...)
Arguments
object |
An object of class ProxGrad. |
... |
Additional arguments for compatibility. |
Value
The coefficients for the ProxGrad object.
Author(s)
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
See Also
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)
# 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)
# Coefficients
coef(proxgrad.out)
Coefficients for cv.CPGLIB Object
Description
coef.cv.CPGLIB returns the coefficients for a cv.CPGLIB object.
Usage
## S3 method for class 'cv.CPGLIB'
coef(object, groups = NULL, ensemble_average = FALSE, ...)
Arguments
object |
An object of class cv.CPGLIB. |
groups |
The groups in the ensemble for the coefficients. Default is all of the groups in the ensemble. |
ensemble_average |
Option to return the average of the coefficients over all the groups in the ensemble. Default is FALSE. |
... |
Additional arguments for compatibility. |
Value
The coefficients for the cv.CPGLIB object. Default is FALSE.
Author(s)
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
See Also
Examples
# Data simulation
set.seed(1)
n <- 50
N <- 2000
p <- 300
beta.active <- c(abs(runif(p, 0, 1/2))*(-1)^rbinom(p, 1, 0.3))
# Parameters
p.active <- 150
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)
mean(y.test)
# CV CPGLIB - Multiple Groups
cpg.out <- cv.cpg(x.train, y.train,
glm_type = "Logistic",
G = 5, include_intercept = TRUE,
alpha_s = 3/4, alpha_d = 1,
n_lambda_sparsity = 100, n_lambda_diversity = 100,
tolerance = 1e-5, max_iter = 1e5)
cpg.coef <- coef(cpg.out)
# Coefficients for each group
cpg.coef <- coef(cpg.out, ensemble_average = FALSE)
Coefficients for cv.ProxGrad Object
Description
coef.cv.ProxGrad returns the coefficients for a cv.ProxGrad object.
Usage
## S3 method for class 'cv.ProxGrad'
coef(object, ...)
Arguments
object |
An object of class cv.ProxGrad. |
... |
Additional arguments for compatibility. |
Value
The coefficients for the cv.ProxGrad object.
Author(s)
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
See Also
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)
# 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)
# CV ProxGrad - Single Group
proxgrad.out <- cv.ProxGrad(x.train, y.train,
glm_type = "Logistic",
include_intercept = TRUE,
alpha_s = 3/4,
n_lambda_sparsity = 100,
tolerance = 1e-5, max_iter = 1e5)
# Coefficients
coef(proxgrad.out)
Competing Proximal Gradients Library for Ensembles of Generalized Linear Models
Description
cpg computes the coefficients for ensembles of generalized linear models via competing proximal gradients.
Usage
cpg(
x,
y,
glm_type = c("Linear", "Logistic")[1],
G = 5,
include_intercept = TRUE,
alpha_s = 3/4,
alpha_d = 1,
lambda_sparsity,
lambda_diversity,
tolerance = 1e-08,
max_iter = 1e+05
)
Arguments
x |
Design matrix. |
y |
Response vector. |
glm_type |
Description of the error distribution and link function to be used for the model. Must be one of "Linear" or "Logistic". Default is "Linear". |
G |
Number of groups in the ensemble. |
include_intercept |
Argument to determine whether there is an intercept. Default is TRUE. |
alpha_s |
Sparsity mixing parmeter. Default is 3/4. |
alpha_d |
Diversity mixing parameter. Default is 1. |
lambda_sparsity |
Sparsity tuning parameter value. |
lambda_diversity |
Diversity tuning parameter value. |
tolerance |
Convergence criteria for the coefficients. Default is 1e-8. |
max_iter |
Maximum number of iterations in the algorithm. Default is 1e5. |
Value
An object of class cpg
Author(s)
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
See Also
Examples
# Data simulation
set.seed(1)
n <- 50
N <- 2000
p <- 300
beta.active <- c(abs(runif(p, 0, 1/2))*(-1)^rbinom(p, 1, 0.3))
# Parameters
p.active <- 150
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)
# CPGLIB - Multiple Groups
cpg.out <- cpg(x.train, y.train,
glm_type = "Logistic",
G = 5, include_intercept = TRUE,
alpha_s = 3/4, alpha_d = 1,
lambda_sparsity = 0.01, lambda_diversity = 1,
tolerance = 1e-5, max_iter = 1e5)
# Predictions
cpg.prob <- predict(cpg.out, newx = x.test, type = "prob",
groups = 1:cpg.out$G, ensemble_type = "Model-Avg")
cpg.class <- predict(cpg.out, newx = x.test, type = "prob",
groups = 1:cpg.out$G, ensemble_type = "Model-Avg")
plot(prob.test, cpg.prob, pch = 20)
abline(h = 0.5,v = 0.5)
mean((prob.test-cpg.prob)^2)
mean(abs(y.test-cpg.class))
Generalized Linear Models via Proximal Gradients - Cross-validation
Description
cv.ProxGrad computes and cross-validates the coefficients for generalized linear models using proximal gradients.
Usage
cv.ProxGrad(
x,
y,
glm_type = c("Linear", "Logistic")[1],
include_intercept = TRUE,
alpha_s = 3/4,
n_lambda_sparsity = 100,
tolerance = 1e-08,
max_iter = 1e+05,
n_folds = 10,
n_threads = 1
)
Arguments
x |
Design matrix. |
y |
Response vector. |
glm_type |
Description of the error distribution and link function to be used for the model. Must be one of "Linear" or "Logistic". Default is "Linear". |
include_intercept |
Argument to determine whether there is an intercept. Default is TRUE. |
alpha_s |
Elastic net mixing parmeter. Default is 3/4. |
n_lambda_sparsity |
Sparsity tuning parameter value. Default is 100. |
tolerance |
Convergence criteria for the coefficients. Default is 1e-8. |
max_iter |
Maximum number of iterations in the algorithm. Default is 1e5. |
n_folds |
Number of cross-validation folds. Default is 10. |
n_threads |
Number of threads. Default is a single thread. |
Value
An object of class cv.ProxGrad
Author(s)
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
See Also
coef.cv.ProxGrad, predict.cv.ProxGrad
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)
# 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 Groups
proxgrad.out <- cv.ProxGrad(x.train, y.train,
glm_type = "Logistic",
include_intercept = TRUE,
alpha_s = 3/4,
n_lambda_sparsity = 100,
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))
Competing Proximal Gradients Library for Ensembles of Generalized Linear Models - Cross-Validation
Description
cv.cpg computes and cross-validates the coefficients for ensembles of generalized linear models via competing proximal gradients.
Usage
cv.cpg(
x,
y,
glm_type = c("Linear", "Logistic")[1],
G = 5,
full_diversity = FALSE,
include_intercept = TRUE,
alpha_s = 3/4,
alpha_d = 1,
n_lambda_sparsity = 100,
n_lambda_diversity = 100,
tolerance = 1e-08,
max_iter = 1e+05,
n_folds = 10,
n_threads = 1
)
Arguments
x |
Design matrix. |
y |
Response vector. |
glm_type |
Description of the error distribution and link function to be used for the model. Must be one of "Linear" or "Logistic". Default is "Linear". |
G |
Number of groups in the ensemble. |
full_diversity |
Argument to determine if the overlap between the models should be zero. Default is FALSE. |
include_intercept |
Argument to determine whether there is an intercept. Default is TRUE. |
alpha_s |
Sparsity mixing parmeter. Default is 3/4. |
alpha_d |
Diversity mixing parameter. Default is 1. |
n_lambda_sparsity |
Number of candidates for sparsity tuning parameter. Default is 100. |
n_lambda_diversity |
Number of candidates for diveristy tuning parameter. Default is 100. |
tolerance |
Convergence criteria for the coefficients. Default is 1e-8. |
max_iter |
Maximum number of iterations in the algorithm. Default is 1e5. |
n_folds |
Number of cross-validation folds. Default is 10. |
n_threads |
Number of threads. Default is a single thread. |
Value
An object of class cv.cpg
Author(s)
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
See Also
coef.cv.CPGLIB, predict.cv.CPGLIB
Examples
# Data simulation
set.seed(1)
n <- 50
N <- 2000
p <- 300
beta.active <- c(abs(runif(p, 0, 1/2))*(-1)^rbinom(p, 1, 0.3))
# Parameters
p.active <- 150
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)
# CV CPGLIB - Multiple Groups
cpg.out <- cv.cpg(x.train, y.train,
glm_type = "Logistic",
G = 5, include_intercept = TRUE,
alpha_s = 3/4, alpha_d = 1,
n_lambda_sparsity = 100, n_lambda_diversity = 100,
tolerance = 1e-5, max_iter = 1e5)
# Predictions
cpg.prob <- predict(cpg.out, newx = x.test, type = "prob",
groups = 1:cpg.out$G, ensemble_type = "Model-Avg")
cpg.class <- predict(cpg.out, newx = x.test, type = "class",
groups = 1:cpg.out$G, ensemble_type = "Model-Avg")
plot(prob.test, cpg.prob, pch = 20)
abline(h = 0.5,v = 0.5)
mean((prob.test-cpg.prob)^2)
mean(abs(y.test-cpg.class))
Predictions for CPGLIB Object
Description
predict.CPGLIB returns the predictions for a CPGLIB object.
Usage
## S3 method for class 'CPGLIB'
predict(
object,
newx,
groups = NULL,
ensemble_type = c("Model-Avg", "Coef-Avg", "Weighted-Prob", "Majority-Vote")[1],
class_type = c("prob", "class")[1],
...
)
Arguments
object |
An object of class CPGLIB. |
newx |
New data for predictions. |
groups |
The groups in the ensemble for the predictions. Default is all of the groups in the ensemble. |
ensemble_type |
The type of ensembling function for the models. Options are "Model-Avg", "Coef-Avg" or "Weighted-Prob" for classifications predictions. Default is "Model-Avg". |
class_type |
The type of predictions for classification. Options are "prob" and "class". Default is "prob". |
... |
Additional arguments for compatibility. |
Value
The predictions for the CPGLIB object.
Author(s)
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
See Also
Examples
# Data simulation
set.seed(1)
n <- 50
N <- 2000
p <- 300
beta.active <- c(abs(runif(p, 0, 1/2))*(-1)^rbinom(p, 1, 0.3))
# Parameters
p.active <- 150
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)
# CPGLIB - Multiple Groups
cpg.out <- cpg(x.train, y.train,
glm_type = "Logistic",
G = 5, include_intercept = TRUE,
alpha_s = 3/4, alpha_d = 1,
lambda_sparsity = 0.01, lambda_diversity = 1,
tolerance = 1e-5, max_iter = 1e5)
# Predictions
cpg.prob <- predict(cpg.out, newx = x.test, type = "prob",
groups = 1:cpg.out$G, ensemble_type = "Model-Avg")
cpg.class <- predict(cpg.out, newx = x.test, type = "prob",
groups = 1:cpg.out$G, ensemble_type = "Model-Avg")
plot(prob.test, cpg.prob, pch=20)
abline(h=0.5,v=0.5)
mean((prob.test-cpg.prob)^2)
mean(abs(y.test-cpg.class))
Predictions for ProxGrad Object
Description
predict.ProxGrad returns the predictions for a ProxGrad object.
Usage
## S3 method for class 'ProxGrad'
predict(object, newx, type = c("prob", "class")[1], ...)
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. |
Value
The predictions for the ProxGrad object.
Author(s)
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
See Also
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)
# 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))
Predictions for cv.ProxGrad Object
Description
predict.cv.CPGLIB returns the predictions for a ProxGrad object.
Usage
## S3 method for class 'cv.CPGLIB'
predict(
object,
newx,
groups = NULL,
ensemble_type = c("Model-Avg", "Coef-Avg", "Weighted-Prob", "Majority-Vote")[1],
class_type = c("prob", "class")[1],
...
)
Arguments
object |
An object of class cv.CPGLIB. |
newx |
New data for predictions. |
groups |
The groups in the ensemble for the predictions. Default is all of the groups in the ensemble. |
ensemble_type |
The type of ensembling function for the models. Options are "Model-Avg", "Coef-Avg" or "Weighted-Prob" for classifications predictions. Default is "Model-Avg". |
class_type |
The type of predictions for classification. Options are "prob" and "class". Default is "prob". |
... |
Additional arguments for compatibility. |
Value
The predictions for the cv.CPGLIB object.
Author(s)
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
See Also
Examples
# Data simulation
set.seed(1)
n <- 50
N <- 2000
p <- 300
beta.active <- c(abs(runif(p, 0, 1/2))*(-1)^rbinom(p, 1, 0.3))
# Parameters
p.active <- 150
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)
mean(y.test)
# CV CPGLIB - Multiple Groups
cpg.out <- cv.cpg(x.train, y.train,
glm_type = "Logistic",
G = 5, include_intercept = TRUE,
alpha_s = 3/4, alpha_d = 1,
n_lambda_sparsity = 100, n_lambda_diversity = 100,
tolerance = 1e-5, max_iter = 1e5)
# Predictions
cpg.prob <- predict(cpg.out, newx = x.test, type = "prob",
groups = 1:cpg.out$G, ensemble_type = "Model-Avg")
cpg.class <- predict(cpg.out, newx = x.test, type = "class",
groups = 1:cpg.out$G, ensemble_type = "Model-Avg")
plot(prob.test, cpg.prob, pch = 20)
abline(h = 0.5,v = 0.5)
mean((prob.test-cpg.prob)^2)
mean(abs(y.test-cpg.class))
Predictions for cv.ProxGrad Object
Description
predict.cv.ProxGrad returns the predictions for a ProxGrad object.
Usage
## S3 method for class 'cv.ProxGrad'
predict(object, newx, type = c("prob", "class")[1], ...)
Arguments
object |
An object of class cv.ProxGrad. |
newx |
New data for predictions. |
type |
The type of predictions for binary response. Options are "prob" (default) and "class". |
... |
Additional arguments for compatibility. |
Value
The predictions for the cv.ProxGrad object.
Author(s)
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
See Also
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)
# 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)
# CV ProxGrad - Single Group
proxgrad.out <- cv.ProxGrad(x.train, y.train,
glm_type = "Logistic",
include_intercept = TRUE,
alpha_s = 3/4,
n_lambda_sparsity = 100,
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))