# predict.basad: Basad prediction In basad: Bayesian Variable Selection with Shrinking and Diffusing Priors

## Description

Predict the values of a dependent variable using `basad` on new test data.

## Usage

 ```1 2``` ``` ## S3 method for class 'basad' predict(object, newdata = NULL, ...) ```

## Arguments

 `object` An object of class `basad`. `newdata` Data frame or x-matrix for which to evaluate predictions. `...` Further arguments passed to or from other methods.

## Value

A vector of predicted values for a dependent variable in new test data.

## Author(s)

Qingyan Xiang (qyxiang@bu.edu)

Naveen Narisetty (naveen@illinois.edu)

## References

Narisetty, N. N., & He, X. (2014). Bayesian variable selection with shrinking and diffusing priors. The Annals of Statistics, 42(2), 789-817.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28``` ``` #----------------------------------------------------------- #Generate Data: The simulated high dimensional data #----------------------------------------------------------- n = 100; p = 499; nz = 5 rho1=0.25; rho2=0.25; rho3=0.25 ### correlations Bc = c(0, seq(0.6, 3, length.out = nz), array(0, p - nz)) covr1 = (1 - rho1) * diag(nz) + array(rho1, c(nz, nz)) covr3 = (1 - rho3) * diag(p - nz) + array(rho3, c(p - nz, p - nz)) covr2 = array(rho2, c(nz, p - nz)) covr = rbind(cbind(covr1, covr2), cbind(t(covr2), covr3)) covE = eigen(covr) covsq = covE\$vectors %*% diag(sqrt(covE\$values)) %*% t(covE\$vectors) Xs = matrix(rnorm(n * p), nrow = n); Xn = covsq %*% t(Xs) X = cbind(array(1, n), t(Xn)) Y = X %*% Bc + rnorm(n); X <- X[, 2:ncol(X)] #----------------------------------------------------------- #Run the algorithm and then predict #----------------------------------------------------------- obj <- basad(x = X, y = Y) predict(obj, newdata = X) ```

basad documentation built on Nov. 18, 2021, 1:12 a.m.