Description Usage Arguments Examples
BiG implemented with diffuse Inverse Gamma prior or diffuse Uniform prior for the variance/standard deviation parameters.
1 2 3 4 |
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). |
n_T |
vector of length |
n_p1 |
number of studies belong to platform 1. |
M |
number of MCMC iterations. |
burnin |
number of burn-in iterations. |
prior |
either |
ds, dp |
hyperparameter for the prior distributions of variance parameters for study bias and platform bias respectively. Used only when |
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, sigma_p20 |
initial values for the variance of the platform bias for platform 1 and platform 2 respectively. |
mu0 |
vector of length |
kappa10, kappa20 |
vectors of length |
sigma_s0 |
vector of length |
a, b |
hyperparameters for the prior distributions of standard deviation parameters. Used only when |
1 2 3 4 5 | 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))
rank(-BiG_diffuse(r=sim$r, n_T=sim$n_T, n_p1=3, M=100, burnin=50, prior="IG"))
#rank(-BiG_diffuse(r=sim$r, n_T=sim$n_T, n_p1=3, M=100, burnin=50, prior="uniform"))
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