View source: R/predict.cv.multiview.R
predict.cv.multiview | R Documentation |
This function makes predictions from a cross-validated multiview model, using
the stored "multiview"
object, and the optimal value chosen for
lambda
.
## S3 method for class 'cv.multiview'
predict(object, newx, s = c("lambda.1se", "lambda.min"), ...)
object |
Fitted |
newx |
List of new view matrices at which predictions are to be made. |
s |
Value(s) of the penalty parameter |
... |
Not used. Other arguments to predict. |
This function makes it easier to use the results of cross-validation to make a prediction.
The object returned depends on the ... argument which is passed
on to the predict
method for multiview
objects.
# 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
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")
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