predict.sven: Make predictions from a fitted "sven" object.

Description

This function makes point predictions and computes prediction intervals from a fitted "sven" object.

Usage

# S3 method for sven
predict(
  object,
  newdata,
  model = c("WAM", "MAP"),
  interval = c("none", "MC", "Z"),
  return.draws = FALSE,
  Nsim = 10000,
  level = 0.95,
  alpha = 1 - level,
  ...
)

Value

The object returned depends on "interval" argument. If interval = "none", the object is an \(\code{ncol(newdata)}\times 1\) vector of the point predictions; otherwise, the object is an \(\code{ncol(newdata)}\times 3\) matrix with the point predictions in the first column and the lower and upper bounds of prediction intervals in the second and third columns, respectively.

if return.draws is TRUE, a list with the following components is returned:

prediction: vector or matrix as above
mc.draws: an \(\code{ncol(newdata)} \times \code{Nsim}\) matrix of the Monte Carlo samples

Arguments

object: A fitted "sven" object
newdata: Matrix of new values for X at which predictions are to be made. Must be a matrix; can be sparse as in Matrix package.
model: The model to be used to make predictions. Model "MAP" gives the predictions calculated using the MAP model; model "WAM" gives the predictions calculated using the WAM. Default: "WAM".
interval: Type of interval calculation. If interval = "none", only point predictions are returned; if interval = "MC", Monte Carlo prediction intervals are returned; if interval = "Z", Z prediction intervals are returned.
return.draws: only required if interval = "MC". if TRUE, the Monte Carlo samples are returned. Default: FALSE.
Nsim: only required if interval = "MC". The Monte Carlo sample size. Default: 10000.
level: Confidence level of the interval. Default: 0.95.
alpha: Type one error rate. Default: 1-level.
...: Further arguments passed to or from other methods.

Author

Dongjin Li and Somak Dutta
Maintainer: Dongjin Li <dongjl@iastate.edu>

References

Li, D., Dutta, S., Roy, V.(2020) Model Based Screening Embedded Bayesian Variable Selection for Ultra-high Dimensional Settings http://arxiv.org/abs/2006.07561

Examples

Run this code

n = 80; p = 100; nonzero = 5
trueidx <- 1:5
nonzero.value <- c(0.50, 0.75, 1.00, 1.25, 1.50)
TrueBeta = numeric(p)
TrueBeta[trueidx] <- nonzero.value

X <- matrix(rnorm(n*p), n, p)
y <- 0.5 + X %*% TrueBeta + rnorm(n)
res <- sven(X=X, y=y)
newx <- matrix(rnorm(20*p), 20, p)
# predicted values at a new data matrix using MAP model
yhat <- predict(object = res, newdata = newx, model = "MAP", interval = "none")
# 95% Monte Carlo prediction interval using WAM
MC.interval <- predict(object = res, model = "WAM", newdata = newx, interval = "MC", level=0.95)
# 95% Z-prediction interval using MAP model
Z.interval <- predict(object = res, model = "MAP", newdata = newx, interval = "Z", level = 0.95)

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