This function predicts outcomes (Y) given the observed variables (X) and observed covariates (Z), and a model fitted using varbvs.

# S3 method for varbvs
predict(object, X, Z = NULL, …)



Output of function varbvs.


n x p input matrix, in which p is the number of variables, and n is the number of samples for which predictions will be made using the fitted model. X cannot be sparse, and cannot have any missing values (NA).


n x m covariate data matrix, where m is the number of covariates. Do not supply an intercept as a covariate (i.e., a column of ones), because an intercept is automatically included in the regression model. For no covariates, set Z = NULL.

Other arguments to generic predict function. These extra arguments are not used here.


For the logistic regression model, we do not provide classification probabilities \(Pr(Y = 1 | X, Z)\) because these probabilities are not necessarily calibrated under the variational approximation. The predictions are computed by averaging over the hyperparameter settings, treating object$logw as (unnormalized) log-marginal probabilities. See varbvs for more details about correctly using object$logw for approximate numerical integration over the hyperparameters, for example by treating these as importance weights.


Vector containing the predicted outcomes for all samples. For family = "binomial", all vector entries are 0 or 1.


P. Carbonetto and M. Stephens (2012). Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies. Bayesian Analysis 7, 73--108.

See also

varbvs, summary.varbvs


  # See help(varbvs) for examples.