probe (version 1.1)

predict_probe_func: Obtaining predictions, confidence intervals and prediction intervals from probe

Description

A function providing predictions, along with \((1-\alpha)*100\%\) credible, and prediction intervals for new observations.

Usage

predict_probe_func(res, X, Z = NULL, alpha = 0.05, X_2 = NULL)

Value

A dataframe with predictions, credible intervals, and prediction intervals for each new observation.

Arguments

res

The results from the probe function.

X

A matrix containing the predictors on which to apply the probe algorithm

Z

(optional) A matrix or dataframe of predictors not subjected to the sparsity assumption to account for.

alpha

significance level for (\(100(1-\alpha)\%\)) credible and prediction intervals.

X_2

(optional) Square of X matrix.

References

McLain, A. C., Zgodic, A., & Bondell, H. (2022). Sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm. arXiv preprint arXiv:2209.08139. Zgodic, A., Bai, R., Zhang, J., Wang, Y., Rorden, C., & McLain, A. (2023). Heteroscedastic sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm. arXiv preprint arXiv:2309.08783.

Examples

### Example
data(Sim_data)
data(Sim_data_test)
attach(Sim_data)
attach(Sim_data_test)
alpha <- 0.05
plot_ind <- TRUE
adj <- 10

# Run the analysis. Y_test and X_test are included for plotting purposes only
full_res <- probe( Y = Y, X = X, Y_test = Y_test, 
X_test = X_test, alpha = alpha, plot_ind = plot_ind, adj = adj)

# Predicting for test data
pred_res <- predict_probe_func(full_res, X = X_test, alpha = alpha)
sqrt(mean((Y_test - pred_res$Pred)^2))
head(pred_res)