Bayesian mixture models for copy number polymorphisms

Vignettes

- README.md
- Implementation of Bayesian mixture models for copy number estimation
- Overview of CNPBayes package
- Overview of CNPBayes package
- Implementation of Bayesian mixture models for copy number estimation
- Inspecting Convergence of a Model
- Overview of CNPBayes package

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**bafLikelihood:**Calculate the likelihood of the observed B allele frequencies...**batch-method:**Retrieve batches from object.**BatchModelExample:**This data is a simulated example of Batch data**bayesFactor:**Compute the Bayes factor**bic-method:**Calculate BIC of a model**burnin-method:**Number of burnin iterations.**chains-method:**Retrieve simulated chains from model object.**chromosome:**Extract character vector of sequence names**CNPBayes:**Bayesian mixture models for copy number estimation**collapseBatch-method:**Estimate batch from any sample-level surrogate variables that...**consensusCNP:**Identify consensus start and stop coordinates of a copy...**copyNumber:**Extract copy number estimates from a 'CopyNumberModel'**CopyNumber-classes:**Mixture model container where mixture components have been...**CopyNumber-methods:**Constructs a CopyNumberModel from SB, SBP, MB, or MBP models**Defunct-classes:**Defunct classes in CNPBayes**dfr-method:**Accessor for degrees of freedom**downSample:**Down sample the observations in a mixture**eta.0-method:**Retrieve the rate parameter for the tau2 distribution.**extract-methods:**extract estimated parameters at particular iteration of...**findSurrogates-MultiBatch-method:**Estimate batch from any sample-level surrogate variables that...**ggplot-functions:**Trace plots of MCMC chains and mixture model densities**gibbs:**Run a Gibbs sampler on one or multiple types of Bayesian...**Hyperparameters:**Create an object of class 'Hyperparameters'**HyperparametersBatch-class:**An object to specify the hyperparameters of a batch effect...**Hyperparameters-class:**An object to specify the hyperparameters of a model.**HyperparametersMarginal-class:**An object to specify the hyperparameters of a marginal model.**HyperparametersMultiBatch:**Create an object of class 'HyperparametersMultiBatch' for the...**HyperparametersMultiBatch-class:**An object to specify the hyperparameters of a batch effect...**HyperparametersSingleBatch:**Create an object of class 'HyperparametersSingleBatch' for...**HyperparametersSingleBatch-class:**An object to specify the hyperparameters of a marginal model.**HyperparametersTrios:**Create an object of class 'Hyperparameters' with additional...**HyperparametersTrios-class:**An object to specify the hyperparameters of a model with...**hyperParams-method:**Accessor for Hyperparameters object for a...**iter-method:**Reset number of iterations.**k-method:**Number of components.**label_switch:**Accessor for determing whether label switching occurred...**logBayesFactor:**Compute the log bayes factor between models.**log_lik-method:**Retrieve log likelihood.**logPrior-method:**Calculate log likelihood of prior for model**m2.0-method:**Retrieve the shape parameter for the tau2 distribution.**mapCnProbability:**Probabiliistic copy number assigments.**mapParams:**Parameters for mapping mixture components to distinct copy...**mapping:**Map mixture components to copy number states**map_z:**Calculate the maximum a posteriori estimate of latent...**marginal_lik:**Accessor for the log marginal likelihood of a SB, SBP, MB, or...**marginalLik:**Extract marginal likelihoods from a list of models**marginalLikelihood-method:**Compute the marginal likelihood of a converged model.**MarginalModelExample:**This data is a simulated example of Marginal data**MBP:**Constructor for MultiBatchPooled model**McmcChains-class:**An object to hold estimated paraeters.**Browse all...**

Description Usage Arguments Value

Models of varying component sizes are compared. The log bayes factor is
calculated comparing each set of two models by marginal likelihood,
as computed by `marginalLikelihood`

.

1 |

`x` |
the result of a call to |

Log Bayes factor comparing the two models with highest likelihood.

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