Defunct-classes: Defunct classes in CNPBayes
In CNPBayes: Bayesian mixture models for copy number polymorphisms

The following classes in CNPBayes are deprecated and are provided only for compatability.

component: The component densities.
overall: The overall (marginal across batches and components) estimate of the density.
modes: A numeric vector providing the estimated modes in the overall density. The modes are defined by a crude estimate of the first derivative of the overall density (see findModes).
data: A numeric vector containing the data
clusters: A vector providing the k-means clustering of the component means using the modes as centers. If an object of class DensityModel is instantiated with merge=FALSE, this slot takes values 1, ..., K, where K is the number of components.
k: An integer value specifying the number of latent classes.
hyperparams: An object of class 'Hyperparameters' used to specify the hyperparameters of the model.
theta: the means of each component and batch
sigma2: the variances of each component and batch
nu.0: the shape parameter for sigma2
sigma2.0: the rate parameter for sigma2
pi: mixture probabilities which are assumed to be the same for all batches
mu: overall mean
tau2: overall variance
data: the data for the simulation.
data.mean: the empirical means of the components
data.prec: the empirical precisions
z: latent variables
zfreq: table of latent variables
probz: n x k matrix of probabilities
logprior: log likelihood of prior: log(p(sigma2.0)p(nu.0)p(mu))
loglik: log likelihood: ∑ p_k Φ(θ_k, σ_k)
mcmc.chains: an object of class 'McmcChains' to store MCMC samples
batch: a vector of the different batch numbers
batchElements: a vector labeling from which batch each observation came from
modes: the values of parameters from the iteration which maximizes log likelihood and log prior
mcmc.params: An object of class 'McmcParams'
label_switch: length-one logical vector indicating whether label-switching occurs (possibly an overfit model)
.internal.constraint: Constraint on parameters. For internal use only.
k: An integer value specifying the number of latent classes.
hyperparams: An object of class 'Hyperparameters' used to specify the hyperparameters of the model.
theta: the means of each component and batch
sigma2: the variances of each component and batch
nu.0: the shape parameter for sigma2
sigma2.0: the rate parameter for sigma2
pi: mixture probabilities which are assumed to be the same for all batches
mu: means from batches, averaged across batches
tau2: variances from batches, weighted by precisions
data: the data for the simulation.
data.mean: the empirical means of the components
data.prec: the empirical precisions
z: latent variables
zfreq: table of latent variables
probz: n x k matrix of probabilities
logprior: log likelihood of prior: log(p(sigma2.0)p(nu.0)p(mu))
loglik: log likelihood: ∑ p_k Φ(θ_k, σ_k)
mcmc.chains: an object of class 'McmcChains' to store MCMC samples
batch: a vector of the different batch numbers
batchElements: a vector labeling from which batch each observation came from
modes: the values of parameters from the iteration which maximizes log likelihood and log prior
label_switch: length-one logical vector indicating whether label-switching occurs (possibly an overfit model)
mcmc.params: An object of class 'McmcParams'
.internal.constraint: Constraint on parameters. For internal use only.
k: An integer value specifying the number of latent classes.
hyperparams: An object of class 'Hyperparameters' used to specify the hyperparameters of the model.
theta: the means of each component and batch
sigma2: the variances of each component and batch
nu.0: the shape parameter for sigma2
sigma2.0: the rate parameter for sigma2
pi: mixture probabilities which are assumed to be the same for all batches
mu: means from batches, averaged across batches
tau2: variances from batches, weighted by precisions
data: the data for the simulation.
data.mean: the empirical means of the components
data.prec: the empirical precisions
z: latent variables
zfreq: table of latent variables
probz: n x k matrix of probabilities
logprior: log likelihood of prior: log(p(sigma2.0)p(nu.0)p(mu))
loglik: log likelihood: ∑ p_k Φ(θ_k, σ_k)
mcmc.chains: an object of class 'McmcChains' to store MCMC samples
batch: a vector of the different batch numbers
batchElements: a vector labeling from which batch each observation came from
modes: the values of parameters from the iteration which maximizes log likelihood and log prior
label_switch: length-one logical vector indicating whether label-switching occurs (possibly an overfit model)
mcmc.params: An object of class 'McmcParams'
.internal.constraint: Constraint on parameters. For internal use only.
k: An integer value specifying the number of latent classes.
hyperparams: An object of class 'Hyperparameters' used to specify the hyperparameters of the model.
theta: the means of each component and batch
sigma2: the variances of each component and batch
nu.0: the shape parameter for sigma2
sigma2.0: the rate parameter for sigma2
pi: mixture probabilities which are assumed to be the same for all batches
mu: overall mean
tau2: overall variance
data: the data for the simulation.
data.mean: the empirical means of the components
data.prec: the empirical precisions
z: latent variables
zfreq: table of latent variables
probz: n x k matrix of probabilities
logprior: log likelihood of prior: log(p(sigma2.0)p(nu.0)p(mu))
loglik: log likelihood: ∑ p_k Φ(θ_k, σ_k)
mcmc.chains: an object of class 'McmcChains' to store MCMC samples
batch: a vector of the different batch numbers
batchElements: a vector labeling from which batch each observation came from
modes: the values of parameters from the iteration which maximizes log likelihood and log prior
mcmc.params: An object of class 'McmcParams'
label_switch: length-one logical vector indicating whether label-switching occurs (possibly an overfit model)
.internal.constraint: Constraint on parameters. For internal use only.