Description Usage Arguments Value See Also Examples
bpr_gibbs.matrix
computes the posterior of the BPR model using auxiliary
variable approach. Since we cannot compute the posterior analytically, a
Gibbs sampling scheme is used.
1 2 3 4 
x 
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 vector of parameters (i.e. coefficients of the basis functions) containing the MLE estimates. 
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. 
... 
Additional parameters 
A list containing the following elements:
w_opt
: Optimized values for the coefficient vector w.
The length of the result is the same as the length of the vector w.
basis
: The basis object.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  basis < polynomial.object(M=2)
w < c(0.1, 0.1, 0.1)
w_0_mean < rep(0, length(w))
w_0_cov < diag(10, length(w))
data < bpr_data[[1]]
out_opt < bpr_gibbs(x = data, w_mle = w, w_0_mean = w_0_mean,
w_0_cov = w_0_cov, basis = basis)
basis < polynomial.object(M=0)
w < c(0.1)
w_0_mean < rep(0, length(w))
w_0_cov < diag(10, length(w))
data < bpr_data[[1]]
out_opt < bpr_gibbs(x = data, w_mle = w, w_0_mean = w_0_mean,
w_0_cov = w_0_cov, basis = basis)

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