This function predicts outcomes (Y) given the observed
variables (X) and observed covariates (Z), and a model fitted using
# S3 method for varbvs predict(object, X, Z = NULL, …)
Output of function
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
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
object$logw as (unnormalized) log-marginal
varbvs for more details about
object$logw for approximate numerical integration
over the hyperparameters, for example by treating these as importance
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 help(varbvs) for examples.