Prediction function for fitted cross validation oem objects

# S3 method for cv.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 `CsparseMatrix` objects of the Matrix package This argument is not used for `type = c("coefficients","nonzero")`
which.model	If multiple penalties are fit and returned in the same `oem` object, the `which.model` argument is used to specify which model to make predictions for. For example, if the oem object `"oemobj"` was fit with argument `penalty = c("lasso", "grp.lasso")`, then `which.model = 2` provides predictions for the group lasso model. For `predict.cv.oem()`, can specify `"best.model"` to use the best model as estimated by cross-validation
s	Value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create the model. For `predict.cv.oem()`, can also specify `"lambda.1se"` or `"lambda.min"` for best lambdas estimated by cross validation
...	used to pass the other arguments for predict.oem

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 <- cv.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))
#> [1] 9.091859

preds.gl <- predict(fit, newx = x.test, type = "response", which.model = "grp.lasso")

apply(preds.gl, 2, function(x) mean((y.test - x) ^ 2))
#> [1] 9.091859

preds.l <- predict(fit, newx = x.test, type = "response", which.model = 1)

apply(preds.l, 2, function(x) mean((y.test - x) ^ 2))
#> [1] 9.099376