xyz (version 0.1)

xyz_regression: Elasticnet with interactions (glmnet)

Description

Elasticnet with interactions (glmnet)

Usage

xyz_regression(X, Y, lambdas = NULL, n_lambda = 10, alpha = 0.9, L = 10, standardize = TRUE, standardize_response = TRUE)

Arguments

X

A matrix.

Y

A vector.

lambdas

A vector of decreasing real numbers containing user specified values of lambda.

n_lambda

A natural number indicating how long the path of lambdas should be.

alpha

A real number between 0 and 1 (the elastic net parameter)

L

An integer indicating how many projection steps are performed.

standardize

A boolean indicating if X should be scaled and centered.

standardize_response

A boolean indicating if Y should be scaled and centered.

Value

N strongest interactions (of type type) between X and Y after L projections.

References

G. Thanei, N. Meinshausen and R. Shah (2016). The xyz algorithm for fast interaction search in high-dimensional data.

Examples

n<-300
p<-1000
#build matrix of predictors
X<-matrix(rnorm(n*p),n,p)
#build a main effect and an interaction into Y
Y<-4*X[,1]*X[,2]-5*X[,4]+rnorm(n)
result<-xyz_regression(X,Y,n_lambda=10,alpha=0.9,L=10)
#print the result
print(result)
#plot the result
plot(result)