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,
type = c("link","response","class"),
averaged = TRUE, ...)When averaged = TRUE, the output is a vector containing the
predicted outcomes for all samples. For family = "binomial",
all vector entries are 0 or 1.
When averaged = FALSE, the return value is a matrix with one
row for each sample, and one column for each hyperparameter setting.
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.
Type of prediction to output. The default, "link", gives
the linear predictors for family = "binomial", and gives the
fitted values for family = "gaussian". For logistic
regression (family = "binomial"), there are two alternative
predictions: "response" givees the fitted probabilities, and "class"
produces the maximum-probability outcome (0 or 1).
When averaged = TRUE, the predictions are
computed by averaging over the hyperparameter settings, treating
object$logw as (unnormalized) log-marginal
probabilities. (See varbvs for more details about
averaging.) When averaged = FALSE, the predictions are
returned as a matrix when one row for each data sample, and one
column for each hyperparameter setting.
Other arguments to generic predict function. These extra arguments are not used here.
Peter Carbonetto peter.carbonetto@gmail.com
Note that the classification probabilities \(Pr(Y = 1 | X, Z, \theta)\) are not guaranteed to be calibrated under the variational approximation.
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.
varbvs, summary.varbvs
# See help(varbvs) for examples.
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