predict.xval.oem {oem} | R Documentation |
Prediction function for fitted cross validation oem objects
Description
Prediction function for fitted cross validation oem objects
Usage
## S3 method for class 'xval.oem'
predict(
object,
newx,
which.model = "best.model",
s = c("lambda.min", "lambda.1se"),
...
)
Arguments
object |
fitted "cv.oem" model object |
newx |
Matrix of new values for x at which predictions are to be made. Must be a matrix; can be sparse as in the
|
which.model |
If multiple penalties are fit and returned in the same |
s |
Value(s) of the penalty parameter |
... |
used to pass the other arguments for |
Value
An object depending on the type argument
Examples
set.seed(123)
n.obs <- 1e4
n.vars <- 100
n.obs.test <- 1e3
true.beta <- c(runif(15, -0.5, 0.5), rep(0, n.vars - 15))
x <- matrix(rnorm(n.obs * n.vars), n.obs, n.vars)
y <- rnorm(n.obs, sd = 3) + x %*% true.beta
x.test <- matrix(rnorm(n.obs.test * n.vars), n.obs.test, n.vars)
y.test <- rnorm(n.obs.test, sd = 3) + x.test %*% true.beta
fit <- xval.oem(x = x, y = y,
penalty = c("lasso", "grp.lasso"),
groups = rep(1:10, each = 10),
nlambda = 10)
preds.best <- predict(fit, newx = x.test, type = "response", which.model = "best.model")
apply(preds.best, 2, function(x) mean((y.test - x) ^ 2))
preds.gl <- predict(fit, newx = x.test, type = "response", which.model = "grp.lasso")
apply(preds.gl, 2, function(x) mean((y.test - x) ^ 2))
preds.l <- predict(fit, newx = x.test, type = "response", which.model = 1)
apply(preds.l, 2, function(x) mean((y.test - x) ^ 2))