Description Usage Arguments Value Author(s) References
View source: R/EstimateTruncatedDictionary.R
Given a matrix with methylation sites as rows and samples as columns, returns a collection of Gaussian kernels truncated between 0 and 1. These kernels are used as a dictionary, where the distribution of values at each site is represented a weighted combination of the kernels.
1 | EstimateTruncatedDictionary(X, K=2, a0 = 0.5,b0 = 0.5,mu0 = 0.5,Concentration = 0.5, NumDraws = 1000)
|
X |
A matrix in which rows represent variables (e.g., methylation sites) and columns represent samples. The entries of the matrix must be continuous between 0 and 1. |
K |
The number of kernels |
a0,b0 |
Gamma hyperperameters for the precision (inverse of the variance) of the kernels. Defaults are a0=0.5, b0=0.5 (we recommend using these defaults if unsure). |
mu0 |
Normal mean hyperparameter for the kernel means. Default is mu0=0.5. |
Concentration |
Initial value of the Dirichlet concentration parameter. Default is 0.5. Can be a vector of length K. |
NumDraws |
Number of MCMC draws for posterior inference. |
Returns an object with the following values, averaged over the MCMC iterations:
Mu |
Vector of length K giving the mean of each kernel |
Sigma |
Vector of length K giving the standard devitation of each kernel |
Concentration |
Vector of length K giving the concentration hyperparameter |
Eric F. Lock
Lock, E. F. & Dunson, D. B. (2015). Shared kernel Bayesian screening. Biometrika, 102 (4): 829-842.
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