BiG_DA: BiG with half-t prior through a data augmentation approach

In BiG: Bayesian Aggregation in Genomic Applications

Description

BiG implemented with half-t prior for the standard deviation parameters of the platform bias and diffuse uniform prior for the variance parameters of the study bias.

Usage

BiG_DA(r, n_T, n_p1, M = 20000, burnin, a = 0.0202, b = 98.5025, dp = 1,
  W = init_W(r), sigma_p10 = 0.5, sigma_p20 = 0.5, mu0 = numeric(G),
  xi10 = numeric(G), xi20 = numeric(G), sigma_s0 = rep(1, S))

Arguments

r	G*S matrix that contains the ranked lists to be aggregated, where G is the total number of items (genes) and S is the total number of ranked lists (studies). NA indicates non-inclusion of item. Note the matrix needs to be arranged such that all lists that belong to the same platform are next to each other, i.e. the first n_p1 columns are lists from platform 1.
n_T	vector of length S that contains number of top ranked items in each study.
n_p1	number of studies belong to platform 1.
M	number of MCMC iterations.
burnin	number of burn-in iterations.
a	hyperparameters for the prior distributions of standard deviation parameters. Used only when prior="uniform".
b	hyperparameters for the prior distributions of standard deviation parameters. Used only when prior="uniform".
dp	hyperparameter for the prior distributions of variance parameters for study bias and platform bias respectively. Used only when prior="IG".
W	G*S matrix that contains initial values for W. Each element of W is the local importance of the corresponding item in the corresponding study, i.e. the latent variable that determines the observed rank.
sigma_p10	initial values for the variance of the platform bias for platform 1 and platform 2 respectively.
sigma_p20	initial values for the variance of the platform bias for platform 1 and platform 2 respectively.
mu0	vector of length G that contains initial values for mu. Each element of mu is global importance of the corresponding item, i.e. the latent variable that determines the true rank.
xi10, xi20	vectors of length G that contain initial values for xi1 and xi2.
sigma_s0	vector of length S that contains initial values for the variances of the study bias.

Examples

set.seed(1234)
sim = sim_lvm(G=25, S=6, n_p1=3, rho=runif(6,min=0.3,max=0.9), p_p1=0.6, p_p2=0.8, 
      lambda=runif(6,min=0.6,max=0.8), n_T=sample(c(5,10,15),6,replace=TRUE))
BiG_DA(r=sim$r, n_T=sim$n_T, n_p1=3, M=100, burnin=50)

BiG documentation built on Nov. 17, 2017, 4:48 a.m.