Description Usage Arguments Value See Also Examples
bpr_gibbs.list
computes the posterior of the BPR model using auxiliary
variable approach. Since we cannot compute the posterior analytically, a
Gibbs sampling scheme is used. This method calls
bpr_gibbs.matrix
to process each element of the list.
1 2 3 4 5 |
x |
A list of elements of length N, where each element is an L x 3 matrix of observations, where 1st column contains the locations. The 2nd and 3rd columns contain the total trials and number of successes at the corresponding locations, repsectively. |
w_mle |
A matrix of MLE estimates for the regression coefficients for each genomic region of interest. |
basis |
A 'basis' object. See |
fit_feature |
Additional feature on how well the profile fits the methylation data. |
cpg_dens_feat |
Additional feature for the CpG density across the promoter region. |
w_0_mean |
The prior mean hyperparameter for w |
w_0_cov |
The prior covariance hyperparameter for w |
gibbs_nsim |
Optional argument giving the number of simulations of the Gibbs sampler. |
gibbs_burn_in |
Optional argument giving the burn in period of the Gibbs sampler. |
is_parallel |
Logical, indicating if code should be run in parallel. |
no_cores |
Number of cores to be used, default is max_no_cores - 2. |
... |
Additional parameters |
A list containing the following elements:
W_opt
: An Nx(M+1) matrix with the optimized parameter values.
Each row of the matrix corresponds to each element of the list x. The
columns are of the same length as the parameter vector w (i.e. number
of basis functions).
Mus
: An N x M matrix with the RBF centers if basis object is
rbf.object
, otherwise NULL.
basis
: The basis object.
w
: The initial values of the parameters w.
x_extrema
: The min and max values of each promoter region.
1 2 3 | ex_data <- bpr_data
basis <- rbf.object(M=3)
out_opt <- bpr_gibbs(x = ex_data, is_parallel = FALSE, basis = basis)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.