Internal varbvs functions
var1(x)
var1.cols(X)
varbvspve(fit,X,nr = 1000)
varbvsnorm(X,y,sigma,sa,logodds,alpha,mu,update.order,tol = 1e-4,
maxiter = 1e4,verbose = TRUE,outer.iter = NULL,
update.sigma = TRUE,update.sa = TRUE,n0 = 10,sa0 = 1)
varbvsbin(X,y,sa,logodds,alpha,mu,eta,update.order,tol = 1e-4,
maxiter = 1e4,verbose = TRUE,outer.iter = NULL,
update.sa = TRUE,optimize.eta = TRUE,n0 = 10,sa0 = 1)
varbvsbinz(X,Z,y,sa,logodds,alpha,mu,eta,update.order,tol = 1e-4,
maxiter = 1e4,verbose = TRUE,outer.iter = NULL,
update.sa = TRUE,optimize.eta = TRUE,n0 = 10,sa0 = 1)
Peter Carbonetto <peter.carbonetto@gmail.com>
These functions are only intended to be used by expert users. Here we provide brief descriptions of some of these internal functions.
var1(x)
returns the second moment of vector x about its mean.
var1.cols(X)
computes the second moment of each column of X about
its mean.
varbvspve
draws posterior estimates of the proportion of
variance in Y explained by the Bayesian variable selection model
fitted using a variational approximation. This function is only valid
for the linear regression model (family = "gaussian")
.
Functions varbvsnorm
, varbvsbin
and varbvsbinz
implement the co-ordinate ascent algorithm to fit the fully-factorized
variational approximation for Bayesian variable selection, conditioned
on settings of the hyperparameters. These functions implement the
algorithm for the linear regression, logistic regression with an
intercept, and logistic regression with arbitrary covariates,
respectively.