coef.cv.multiview {multiview}

Extract coefficients from a cv.multiview object

Description

Extract coefficients from a cv.multiview object

Usage

## S3 method for class 'cv.multiview'
coef(object, s = c("lambda.1se", "lambda.min"), ...)

Arguments

object

Fitted "cv.multiview" object.

s

Value(s) of the penalty parameter lambda at which predictions are required. Default is the value s="lambda.1se" stored on the CV object. Alternatively s="lambda.min" can be used. If s is numeric, it is taken as the value(s) of lambda to be used. (For historical reasons we use the symbol 's' rather than 'lambda' to reference this parameter.)

...

This is the mechanism for passing arguments like ⁠x=⁠ when exact=TRUE; see exact argument.

Value

the matrix of coefficients for specified lambda.

Examples

set.seed(1)
x = matrix(rnorm(100*20), 100, 20)
z = matrix(rnorm(100*20), 100, 20)
U = matrix(rnorm(100*5), 100, 5)
for (m in seq(5)){
    u = rnorm(100)
    x[, m] = x[, m] + u
    z[, m] = z[, m] + u
    U[, m] = U[, m] + u}
x = scale(x, center = TRUE, scale = FALSE)
z = scale(z, center = TRUE, scale = FALSE)
beta_U = c(rep(0.1, 5))
y = U %*% beta_U + 0.1 * rnorm(100)
fit1 = cv.multiview(list(x=x,z=z), y, rho = 0.3)
coef(fit1, s="lambda.min")

# Binomial

by = 1 * (y > median(y)) 
fit2 = cv.multiview(list(x=x,z=z), by, family = binomial(), rho = 0.9)
coef(fit2, s="lambda.min")

# Poisson
py = matrix(rpois(100, exp(y))) 
fit3 = cv.multiview(list(x=x,z=z), py, family = poisson(), rho = 0.6)
coef(fit3, s="lambda.min")