multiview (version 0.8)

predict.cv.multiview: Make predictions from a "cv.multiview" object.

Description

This function makes predictions from a cross-validated multiview model, using the stored "multiview" object, and the optimal value chosen for lambda.

Usage

# S3 method for cv.multiview
predict(object, newx, s = c("lambda.1se", "lambda.min"), ...)

Value

The object returned depends on the ... argument which is passed on to the predict method for multiview objects.

Arguments

object

Fitted "cv.multiview" or object.

newx

List of new view matrices at which predictions are to be made.

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)

...

Not used. Other arguments to predict.

Details

This function makes it easier to use the results of cross-validation to make a prediction.

Examples

# Gaussian
# Generate data based on a factor model
set.seed(1)
x = matrix(rnorm(100*10), 100, 10)
z = matrix(rnorm(100*10), 100, 10)
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)
predict(fit1, newx = list(x[1:5, ],z[1:5,]), s = "lambda.min")

# Binomial
# \donttest{
by = 1 * (y > median(y)) 
fit2 = cv.multiview(list(x=x,z=z), by, family = binomial(), rho = 0.9)
predict(fit2, newx = list(x[1:5, ],z[1:5,]), s = "lambda.min", type = "response")

# Poisson
py = matrix(rpois(100, exp(y))) 
fit3 = cv.multiview(list(x=x,z=z), py, family = poisson(), rho = 0.6)
predict(fit3, newx = list(x[1:5, ],z[1:5,]), s = "lambda.min", type = "response")
# }