TFRE (version 0.1.0)

predict.TFRE: Make predictions from a 'TFRE' object

Description

Make predictions for new X values from a fitted TFRE Lasso, SCAD or MCP model.

Usage

# S3 method for TFRE
predict(object, newX, s, ...)

Value

A vector of predictions for the new X values given the fitted TFRE model.

Arguments

object

Fitted "TFRE" model object.

newX

Matrix of new values for X at which predictions are to be made.

s

Regression model to use for prediction. Should be one of "1st" and "2nd". See more details in "Details".

...

Not used. Other arguments to predict.

Author

Yunan Wu and Lan Wang
Maintainer: Yunan Wu <[e-mail]>

Details

If object$second_stage = "none", s cannot be "2nd". If object$second_stage = "none" and s = "2nd", the function will return the predictions based on the TFRE Lasso regression. If object$second_stage = "scad" or "mcp", and s = "2nd", the function will return the predictions based on the TFRE SCAD or MCP regression with the smallest HBIC.

References

Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), A Tuning-free Robust and Efficient Approach to High-dimensional Regression, Journal of the American Statistical Association, 115:532, 1700-1714, tools:::Rd_expr_doi("10.1080/01621459.2020.1840989").

See Also

TFRE, coef.TFRE, plot.TFRE

Examples

n <- 20; p <- 50
beta0 <- c(1.5,-1.25,1,-0.75,0.5,rep(0,p-5))
eta_list <- 0.1*6:15*sqrt(log(p)/n)
X <- matrix(rnorm(n*p),n)
y <- X %*% beta0 + rt(n,4)
newX <- matrix(rnorm(10*p),10)

# \donttest{
Obj_TFRE_Lasso <- TFRE(X, y, second_stage = "none", const_incomplete = 5)
predict(Obj_TFRE_Lasso, newX, "1st")
predict(Obj_TFRE_Lasso, newX, "2nd")# }

Obj_TFRE_SCAD <- TFRE(X, y, eta_list = eta_list, const_incomplete = 5)
predict(Obj_TFRE_SCAD, newX, "1st")
predict(Obj_TFRE_SCAD, newX, "2nd")

# \donttest{
Obj_TFRE_MCP <- TFRE(X, y, second_stage = "mcp", eta_list = eta_list, const_incomplete = 5)
predict(Obj_TFRE_MCP, newX, "1st")
predict(Obj_TFRE_MCP, newX, "2nd")# }