bravo

README code{white-space: pre-wrap;} span.smallcaps{font-variant: small-caps;} span.underline{text-decoration: underline;} div.column{display: inline-block; vertical-align: top; width: 50%;} div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} ul.task-list{list-style: none;} bravo An R package that performs Bayesian iterated screening and/or variable selection for ultra-high dimensional Gaussian linear regression models

Help for package bravo const macros = { "\\R": "\\textsf{R}", "\\mbox": "\\text", "\\code": "\\texttt"}; function processMathHTML() { var l = document.getElementsByClassName('reqn'); for (let e of l) { katex.render(e.textContent, e, { throwOnError: false, macros }); } return; } Package {bravo} Contents bits mip.sven predict.sven sven Type: Package Title: Bayesian Screening and Variable Selection Version: 3.2.2 Maintainer: Dongjin Li <liyangxiaobei@gmail.com> Description: Performs Bayesian variable screening and selection for ultra-high dimensional linear regression models. License: GPL-3 Encoding: UTF-8 Depends: R (≥ 3.0.2), methods Imports: Rcpp (≥ 1.0.2), Matrix (≥ 1.2-17) LinkingTo: Rcpp NeedsCompilation: yes RoxygenNote: 7.3.1 Packaged: 2024-10-29 22:02:34 UTC; dongjl Author: Dongjin Li [aut, cre], Debarshi Chakraborty [aut], Somak Dutta [aut], Vivekananda Roy [ctb] Repository: CRAN Date/Publication: 2024-10-29 23:10:06 UTC Bayesian Iterated Screening (ultra-high, high or low dimensional). Description Perform Bayesian iterated screening in Gaussian regression models Usage bits(X, y, lam = 1, w = 0.5, pp = FALSE, max.var = nrow(X), verbose = TRUE) Arguments X An n\times p matrix. Sparse matrices are supported and every care is taken not to make copies of this (typically) giant matrix. No need to center or scale. y The response vector of length n . lam The slab precision parameter. Default: 1 . w The prior inclusion probability of each variable. Default: 1/2 . pp Boolean: If FALSE (default) the algorithm stops after including max.var many variables. If true, the posterior probability stopping rule is used. max.var The maximum number of variables to be included. verbose If TRUE (default) will show the variable index included in each iteration. Value A list with components model.pp An integer vector of the screened model. postprobs The sequence of posterior probabilities until the last included variable. lam The value of lam, the slab precision parameter. w The value of w, the prior inclusion probability. References Wang, R., Dutta, S., Roy, V. (2021) Bayesian iterative screening in ultra-high dimensional settings. https://arxiv.org/abs/2107.10175 Examples n=50; p=100; TrueBeta <- c(rep(5,3),rep(0,p-3)) rho <- 0.6 x1 <- matrix(rnorm(n*p), n, p) X <- sqrt(1-rho)*x1 + sqrt(rho)*rnorm(n) y <- 0.5 + X %*% TrueBeta + rnorm(n) res<-bits(X,y, pp=TRUE) res$model.pp # the vector of screened model res$postprobs # the log (unnormalized) posterior probabilities corresponding to the model.pp. 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 <somakd@iastate.edu> 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 Make predictions from a fitted "sven" object. Description This function makes point predictions and computes prediction intervals from a fitted "sven" object. Usage ## S3 method for class 'sven' predict( object, newdata, model = c("WAM", "MAP"), interval = c("none", "MC", "Z"), return.draws = FALSE, Nsim = 10000, level = 0.95, alpha = 1 - level, ... ) Arguments object A fitted "sven" object newdata Matrix of new values for X at which predictions are to be made. Must be a matrix; can be sparse as in Matrix package. model The model to be used to make predictions. Model "MAP" gives the predictions calculated using the MAP model; model "WAM" gives the predictions calculated using the WAM. Default: "WAM". interval Type of interval calculation. If interval = "none" , only point predictions are returned; if interval = "MC" , Monte Carlo prediction intervals are returned; if interval = "Z" , Z prediction intervals are returned. return.draws only required if interval = "MC" . if TRUE , the Monte Carlo samples are returned. Default: FALSE . Nsim only required if interval = "MC" . The Monte Carlo sample size. Default: 10000. level Confidence level of the interval. Default: 0.95. alpha Type one error rate. Default: 1- level . ... Further arguments passed to or from other methods. Value The object returned depends on "interval" argument. If interval = "none" , the object is an \code{ncol(newdata)}\times 1 vector of the point predictions; otherwise, the object is an \code{ncol(newdata)}\times 3 matrix with the point predictions in the first column and the lower and upper bounds of prediction intervals in the second and third columns, respectively. if return.draws is TRUE , a list with the following components is returned: prediction vector or matrix as above mc.draws an \code{ncol(newdata)} \times \code{Nsim} matrix of the Monte Carlo samples Author(s) Dongjin Li and Somak Dutta Maintainer: Dongjin Li <dongjl@iastate.edu> References Li, D., Dutta, S., Roy, V.(2020) Model Based Screening Embedded Bayesian Variable Selection for Ultra-high Dimensional Settings http://arxiv.org/abs/2006.07561 Examples n = 80; p = 100; nonzero = 5 trueidx <- 1:5 nonzero.value <- c(0.50, 0.75, 1.00, 1.25, 1.50) TrueBeta = numeric(p) TrueBeta[trueidx] <- nonzero.value X <- matrix(rnorm(n*p), n, p) y <- 0.5 + X %*% TrueBeta + rnorm(n) res <- sven(X=X, y=y) newx <- matrix(rnorm(20*p), 20, p) # predicted values at a new data matrix using MAP model yhat <- predict(object = res, newdata = newx, model = "MAP", interval = "none") # 95% Monte Carlo prediction interval using WAM MC.interval <- predict(object = res, model = "WAM", newdata = newx, interval = "MC", level=0.95) # 95% Z-prediction interval using MAP model Z.interval <- predict(object = res, model = "MAP", newdata = newx, interval = "Z", level = 0.95) Selection of variables with embedded screening using Bayesian methods (SVEN) in Gaussian linear models (ultra-high, high or low dimensional). Description SVEN is an approach to selecting variables with embedded screening using a Bayesian hierarchical model. It is also a variable selection method in the spirit of the stochastic shotgun search algorithm. However, by embedding a unique model based screening and using fast Cholesky updates, SVEN produces a highly scalable algorithm to explore gigantic model spaces and rapidly identify the regions of high posterior probabilities. It outputs the log (unnormalized) posterior probability of a set of best (highest probability) models. For more details, see Li et al. (2023, https://doi.org/10.1080/10618600.2022.2074428) Usage sven( X, y, w = NULL, lam = NULL, Ntemp = 10, Tmax = NULL, Miter = 50, wam.threshold = 0.5, log.eps = -16, L = 20, verbose = FALSE ) Arguments X The n\times p covariate matrix or list of two matrices without intercept. The following classes are supported: matrix and dgCMatrix . Every care is taken not to make copies of these (typically) giant matrices. No need to center or scale these matrices manually. Scaling is performed implicitly and regression coefficient are returned on the original scale. Typically, in a combined GWAS-TWAS type analysis, X[[1]] should be a sparse matrix and X[[2]] should be a dense matrix. y The response vector of length n . No need to center or scal