predict.lsgl: Predict

In nielsrhansen/lsgl: Multivariate Linear Sparse Group Lasso

Predict

Description

Compute the predicted response matrix for a new data set.

Usage

## S3 method for class 'lsgl'
predict(object, x, sparse.data = is(x, "sparseMatrix"), ...)

Arguments

object	an object of class lsgl, produced with lsgl.
x	a data matrix of size N_\textrm{new} \times p.
sparse.data	if TRUE x will be treated as sparse, if x is a sparse matrix it will be treated as sparse by default.
...	ignored.

Value

Yhat	the predicted response matrix (of size N_\textrm{new} \times K)

Author(s)

Martin Vincent

Examples

set.seed(100) # This may be removed, it ensures consistency of the daily tests

## Simulate from Y=XB+E, the dimension of Y is N x K, X is N x p, B is p x K

N <- 100 #number of samples
p <- 50 #number of features
K <- 25  #number of groups

B<-matrix(sample(c(rep(1,p*K*0.1),rep(0, p*K-as.integer(p*K*0.1)))),nrow=p,ncol=K)
X1<-matrix(rnorm(N*p,1,1),nrow=N,ncol=p)
Y1 <-X1%*%B+matrix(rnorm(N*K,0,1),N,K)

X2<-matrix(rnorm(N*p,1,1),nrow=N,ncol=p)
Y2 <-X2%*%B+matrix(rnorm(N*K,0,1),N,K)

#### Fit models using X1
lambda <- lsgl::lambda(X1, Y1, alpha = 1, d = 25L, lambda.min = 0.5, intercept = FALSE)
fit <- lsgl::fit(X1, Y1, alpha = 1, lambda = lambda, intercept = FALSE)

# Predict Y2 using the estimated models and X2
res <- predict(fit, X2)

 # Frobenius norm \|Y2hat - Y2\|_F
sapply(res$Yhat, function(y) sqrt(sum((y - Y2)^2)))

# This is proportional to the errors compute with Err:
Err(fit, X2, Y2)*length(Y2)

nielsrhansen/lsgl documentation built on Feb. 11, 2024, 8:07 a.m.