Internal varbvs functions

Internal varbvs functions

diagsq(X,a = NULL)
diagsqt(X,a = NULL)
var1(x)
var1.cols(X)
varbvspve(X,fit,nr = 1000)
varbvsnorm(X,y,sigma,sa,logodds,alpha,mu,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,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,tol = 1e-4,maxiter = 1e4,
           verbose = TRUE,outer.iter = NULL,update.sa = TRUE,
           optimize.eta = TRUE,n0 = 10,sa0 = 1)

Details

These functions are only intended to be used by expert users. Here we provide brief descriptions of some of these internal functions. diagsq(X,a) and diagsqt(X,a) compute the diagonal entries of the matrix products \(X'*A*X\) and \(X*A*X'\), respectively, where A = diag(a), but the computation is done more efficiently, and without having to store an intermediate matrix of the same size as the matrix.

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.