sim_lvm: Simulate rank data from latent variable model

In BiG: Bayesian Aggregation in Genomic Applications

Description

Simulate rank data from latent variable model

Usage

sim_lvm(G, S, n_p1, rho, p_p1, p_p2, lambda, n_T)

Arguments

G	total number of genes involved in all of the studies.
S	number of studies (ranked lists) to be aggregated.
n_p1	number of studies belong to platform 1.
rho	correlation between local importance (w) and global importance (mu) for each study, which determines the total variance of w.
p_p1, p_p2	percentage of total variance of w contributed by platform variance from platform 1 and platform 2 respectively for the study with the lowest total variance.
lambda	inclusion rate for each study.
n_T	vector of length S that contains number of top ranked items in each study.

Examples

set.seed(1234)
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 documentation built on Nov. 17, 2017, 4:48 a.m.