predict.cv.oem | R Documentation |
Prediction function for fitted cross validation oem objects
## S3 method for class 'cv.oem' predict( object, newx, which.model = "best.model", s = c("lambda.min", "lambda.1se"), ... )
object |
fitted |
newx |
Matrix of new values for |
which.model |
If multiple penalties are fit and returned in the same |
s |
Value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create
the model. For |
... |
used to pass the other arguments for predict.oem |
An object depending on the type argument
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)) 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))
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