Deprecated-classes: Deprecated classes in CNPBayes

Description Slots

Description

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

Run hierarchical MCMC for batch model.

Run marginal MCMC simulation

Slots

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.


CNPBayes documentation built on May 2, 2018, 3:57 a.m.