mip.sven: Compute marginal inclusion probabilities from a fitted "sven"...

In dongjli/bravo: Bayesian Screening and Variable Selection

Compute marginal inclusion probabilities from a fitted "sven" object.

Description

This function computes the marginal inclusion probabilities of all variables from a fitted "sven" object.

Usage

mip.sven(object, threshold = 0)

Arguments

object	A fitted "sven" object
threshold	marginal inclusion probabilities above this threshold are stored. Default 0.

Value

The object returned is a data frame if the sven was run with a single matrix, or a list of two data frames if sven was run with a list of two matrices. The first column are the variable names (or numbers if column names of were absent). Only the nonzero marginal inclusion probabilities are stored.

Author(s)

Somak Dutta
Maintainer: Somak Dutta <[e-mail]>

Examples

n <- 50; p <- 100; nonzero <- 3
trueidx <- 1:3
truebeta <- c(4,5,6)
X <- matrix(rnorm(n*p), n, p) # n x p covariate matrix
y <- 0.5 + X[,trueidx] %*% truebeta + rnorm(n)
res <- sven(X=X, y=y)
res$model.map # the MAP model
mip.sven(res)

Z <- matrix(rnorm(n*p), n, p) # another covariate matrix
y2 = 0.5 + X[,trueidx] %*% truebeta  + Z[,1:2] %*% c(-2,-2) + rnorm(n)
res2 <- sven(X=list(X,Z), y=y2)
mip.sven(res2) # two data frames, one for X and another for Z

dongjli/bravo documentation built on Nov. 27, 2024, 9:50 p.m.