predict.varbvsmix

This function predicts outcomes (Y) given the observed
 variables (X) and observed covariates (Z), and a model fitted using
 <code>varbvsmix</code>.

Fast algorithms for fitting Bayesian variable selection
models and computing Bayes factors, in which the outcome (or
response variable) is modeled using a linear regression or a
logistic regression. The algorithms are based on the variational
approximations described in "Scalable variational inference for
Bayesian variable selection in regression, and its accuracy in
genetic association studies" (P. Carbonetto & M. Stephens, 2012,
<DOI:10.1214/12-BA703>). This software has been applied to large
data sets with over a million variables and thousands of samples.

Peter Carbonetto

varbvs

Large-Scale Bayesian Variable Selection Using Variational
Methods

Matthew Stephens

David Gerard

predict.varbvsmix function

<dl> <dt>object</dt>
<dd>Output of function <code>varbvsmix</code>.</dd>
 <dt>X</dt>
<dd>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).</dd>
 <dt>Z</dt>
<dd>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 <code>Z = NULL</code>.</dd> 
 <dt>...</dt>
<dd>Other arguments to generic predict function. These
 extra arguments are not used here.</dd></dl>

Arguments

Make predictions from a model fitted by varbvsmix. — predict.varbvsmix

<dl>

 <dt>object</dt>
<dd>Output of function <code>varbvsmix</code>.</dd>


 <dt>X</dt>
<dd>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).</dd>


 <dt>Z</dt>
<dd>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 <code>Z = NULL</code>.</dd>

 
 <dt>...</dt>
<dd>Other arguments to generic predict function. These
 extra arguments are not used here.</dd>

</dl>

predict.varbvsmix: Make predictions from a model fitted by varbvsmix.

Description

Usage

Arguments

See Also

Examples