This function allows one to conduct a leave-one-out cross validation study using IVBMA. It takes an appropriately constructed object and proceeds to drop each observation, fit IVBMA using the remaining observations, forms a posterior predictive distribution of the dropped observation and scores the predictive distribution along a number of metrics.
Object containing data, d$Y, d$X, d$W, d$Z must all be defined
additional parameters to be passed to ivbma. In particular, you'll usually want to set s.
This returns an n by 4 matrix. Row i of the matrix gives the squared error (SE), absolute error (AE), predictive variance (VAR) and continous ranked probability score (CRPS) of the posterior predictive distribution leaving observation i out and subsequently using i as the verifying observation.
Anna Karl and Alex Lenkoski (2012). "Instrumental Variable Bayesian Model Averaging via Conditional Bayes Factors" http://arxiv.org/abs/1202.5846
1 2 3 4 5 6
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.