dat: simulated data for demonstrating the features of Bayenet.

In Bayenet: Robust Bayesian Elastic Net

simulated data for demonstrating the features of Bayenet.

Description

Simulated gene expression data for demonstrating the features of Bayenet.

Usage

data("dat")

Format

dat consists of four components: X, Y, clin, coef.

Details

The data model for generating Y

Use subscript i to denote the ith subject. Let (Y_{i}, X_{i}, clin_{i}) (i=1,\ldots,n) be independent and identically distributed random vectors. Y_{i} is a continuous response variable representing the cancer outcome and disease phenotype. X_{i} is the p–dimensional vector of genetic factors. The clinical factors is denoted as the q-dimensional vector clin_{i}. The \epsilon follows some heavy-tailed distribution. Considering the following model:

Y_{i} = \alpha_{0} + \sum_{k=1}^{q}\gamma_{k}C_{ik}+\sum_{j=1}^{p}\beta_{j}X_{ij}+\epsilon_{i},

where \alpha_{0} is the intercept, \gamma_{k}'s and \beta_{j}'s are the regression coefficients corresponding to effects of clinical factors and genetic variants, respectively. Denote \gamma=(\gamma_{1}, \ldots, \gamma_{q})^{T}, \beta=(\beta_{1}, \ldots, \beta_{p})^{T}. Then model can be written as

Y_{i} = C_{i}\gamma + X_{i}\beta + \epsilon_{i}.

See Also

Bayenet

Examples

data(dat)
dim(X)

Bayenet documentation built on April 4, 2025, 12:26 a.m.