Number of outlier at each iteration of MCMC
Source:R/machinelearning-functions-tagm-mcmc-helper.R
mcmc-helpers.RdHelper function to get the number of outlier at each MCMC iteration.
Helper function to get mean component allocation at each MCMC iteration.
Helper function to get mean probability of belonging to outlier at each iteration.
Wrapper for the geweke diagnostics from coda package also return p-values.
Helper function to pool chains together after processing
Helper function to burn n iterations from the front of the chains
Helper function to subsample the chains, known informally as thinning.
Produces a violin plot with the protein posterior probabilities distributions for all organelles.
Usage
mcmc_get_outliers(x)
mcmc_get_meanComponent(x)
mcmc_get_meanoutliersProb(x)
geweke_test(k)
mcmc_pool_chains(param)
mcmc_burn_chains(x, n = 50)
mcmc_thin_chains(x, freq = 5)
# S4 method for class 'MCMCParams,character'
plot(x, y, ...)Arguments
- x
Object of class
MCMCParams- k
A
listof coda::mcmc objects, as returned bymcmc_get_outliers,mcmc_get_meanComponentandmcmc_get_meanoutliersProb.- param
An object of class
MCMCParams.- n
integer(1)defining number of iterations to burn. The default is50- freq
Thinning frequency. The function retains every `freq`th iteration and is an `integer(1)`. The default thinning frequency is `5`.
- y
A `character(1)` with a protein name.
- ...
Currently ignored.