Type:	Package
Title:	Build Dirichlet Process Objects for Bayesian Modelling
Version:	0.4.2
Maintainer:	Dean Markwick <dean.markwick@talk21.com>
Description:	Perform nonparametric Bayesian analysis using Dirichlet processes without the need to program the inference algorithms. Utilise included pre-built models or specify custom models and allow the 'dirichletprocess' package to handle the Markov chain Monte Carlo sampling. Our Dirichlet process objects can act as building blocks for a variety of statistical models including and not limited to: density estimation, clustering and prior distributions in hierarchical models. See Teh, Y. W. (2011) https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf, among many other sources.
Depends:	R (≥ 2.10)
License:	GPL-3
Encoding:	UTF-8
LazyData:	true
Suggests:	testthat, knitr, rmarkdown, tidyr, dplyr
Imports:	gtools, ggplot2, mvtnorm
URL:	https://github.com/dm13450/dirichletprocess, https://dm13450.github.io/dirichletprocess/
BugReports:	https://github.com/dm13450/dirichletprocess/issues
RoxygenNote:	7.2.3
NeedsCompilation:	no
Packaged:	2023-08-25 07:16:21 UTC; deanmarkwick
Author:	Gordon J. Ross [aut], Dean Markwick [aut, cre], Kees Mulder [ctb], Giovanni Sighinolfi [ctb], Filippo Fiocchi [ctb]
Repository:	CRAN
Date/Publication:	2023-08-25 09:40:02 UTC

Create a Beta mixture with zeros at the boundaries.

Description

Create a Beta mixture with zeros at the boundaries.

Usage

BetaMixture2Create(priorParameters = 2, mhStepSize = c(1, 1), maxT = 1)

Arguments

Value

A mixing distribution object.

Create a Beta mixing distribution.

Description

See DirichletProcessBeta for the default prior and hyper prior distributions.

Usage

BetaMixtureCreate(
  priorParameters = c(2, 8),
  mhStepSize = c(1, 1),
  maxT = 1,
  hyperPriorParameters = c(1, 0.125)
)

Arguments

Value

A mixing distribution object.

Add burn-in to a dirichletprocess object

Description

Add burn-in to a dirichletprocess object

Usage

Burn(dpobj, niter)

Arguments

Value

A dirichletprocess object where all chain objects have the first niter iterations are removed.

Examples

dp <- Fit(DirichletProcessGaussian(rnorm(10)), 100)
DiagnosticPlots(dp)
burned_dp <- Burn(dp, 50)
DiagnosticPlots(burned_dp)

Change the observations of fitted Dirichlet Process.

Description

Using a fitted Dirichlet process object include new data. The new data will be assigned to the best fitting cluster for each point.

Usage

ChangeObservations(dpobj, newData)

Arguments

Value

Changed Dirichlet process object

Examples

y <- rnorm(10)
dp <- DirichletProcessGaussian(y)
dp <- ChangeObservations(dp, rnorm(10))

Update the component of the Dirichlet process

Description

Update the cluster assignment for each data point.

Usage

ClusterComponentUpdate(dpObj)

## S3 method for class 'conjugate'
ClusterComponentUpdate(dpObj)

## S3 method for class 'hierarchical'
ClusterComponentUpdate(dpObj)

Arguments

Value

Dirichlet process object with update components.

Examples

dp <- DirichletProcessGaussian(rnorm(10))
dp <- ClusterComponentUpdate(dp)

Predict the cluster labels of some new data.

Description

Given a fitted Dirichlet process object and some new data use this function to predict what clusters the new data belong to and associated cluster parameters.

Usage

ClusterLabelPredict(dpobj, newData)

Arguments

Value

A list of the predicted cluster labels of some new unseen data.

Examples

y <- rnorm(10)
dp <- DirichletProcessGaussian(y)
dp <- Fit(dp, 5)
newY <- rnorm(10, 1)
pred <- ClusterLabelPredict(dp, newY)

Update the cluster parameters of the Dirichlet process.

Description

Update the parameters of each individual cluster using all the data assigned to the particular cluster. A sample is taken from the posterior distribution using a direct sample if the mixing distribution is conjugate or the Metropolis Hastings algorithm for non-conjugate mixtures.

Usage

ClusterParameterUpdate(dpObj)

Arguments

Value

Dirichlet process object with update cluster parameters

Examples

dp <- DirichletProcessGaussian(rnorm(10))
dp <- ClusterParameterUpdate(dp)

Diagnostic plots for dirichletprocess objects

Description

Plot several diagnostic plots for dirichletprocess objects. Because the dimension of the dirichletprocess mixture is constantly changing, it is not simple to create meaningful plots of the sampled parameters. Therefore, the plots focus on the likelihood, alpha, and the number of clusters.

Usage

DiagnosticPlots(dpobj, gg = FALSE)

AlphaTraceplot(dpobj, gg = TRUE)

AlphaPriorPosteriorPlot(
  dpobj,
  prior_color = "#2c7fb8",
  post_color = "#d95f02",
  gg = TRUE
)

ClusterTraceplot(dpobj, gg = TRUE)

LikelihoodTraceplot(dpobj, gg = TRUE)

Arguments

Value

If gg = TRUE, a ggplot2 object. Otherwise, nothing is returned and a base plot is plotted.

Functions

• AlphaTraceplot(): Trace plot of alpha.

• AlphaPriorPosteriorPlot(): Plot of the prior and posterior of alpha.

• ClusterTraceplot(): Trace plot of the number of clusters.

• LikelihoodTraceplot(): Trace plot of the likelihood of the data for each iteration.

Examples

dp <- Fit(DirichletProcessGaussian(rnorm(10)), 100)
DiagnosticPlots(dp)

Create a generic Dirichlet process hidden Markov Model

Description

Create a hidden Markov model where the data is believed to be generated from the mixing object distribution.

Usage

DirichletHMMCreate(x, mdobj, alpha, beta)

Arguments

Dirichlet process mixture of the Beta distribution.

Description

Create a Dirichlet process object using the mean and scale parameterisation of the Beta distribution bounded on (0, maxY).

Usage

DirichletProcessBeta(
  y,
  maxY,
  g0Priors = c(2, 8),
  alphaPrior = c(2, 4),
  mhStep = c(1, 1),
  hyperPriorParameters = c(1, 0.125),
  verbose = TRUE,
  mhDraws = 250
)

Arguments

Details

G_0 (\mu , \nu | maxY, \alpha _0 , \beta _0) = U(\mu | 0, maxY) \mathrm{Inv-Gamma} (\nu | \alpha _0, \beta _0).

The parameter \beta _0 also has a prior distribution \beta _0 \sim \mathrm{Gamma} (a, b) if the user selects Fit(...,updatePrior=TRUE).

Value

Dirichlet process object

Dirichlet process mixture of Beta distributions with a Uniform Pareto base measure.

Description

Create a Dirichlet process object using the mean and scale parameterisation of the Beta distribution bounded on (0, maxY). The Pareto distribution is used as a prior on the scale parameter to ensure that the likelihood is 0 at the boundaries.

Usage

DirichletProcessBeta2(
  y,
  maxY,
  g0Priors = 2,
  alphaPrior = c(2, 4),
  mhStep = c(1, 1),
  verbose = TRUE,
  mhDraws = 250
)

Arguments

Details

G_0 (\mu , \nu | maxY, \alpha ) = U(\mu | 0, maxY) \mathrm{Pareto} (\nu | x_m, \gamma).

Value

Dirichlet process object

Create a Dirichlet Process object

Description

Using a previously created Mixing Distribution Object (mdObject) create a Dirichlet process object. 'alphaPriorParameters sets the parameters for alpha using the shape-rate specification of the gamma distribution.

Usage

DirichletProcessCreate(
  x,
  mdObject,
  alphaPriorParameters = c(1, 1),
  mhDraws = 250
)

Arguments

Create a Dirichlet Mixture of Exponentials

Description

This is the constructor function to produce a dirichletprocess object with a Exponential mixture kernel with unknown rate. The base measure is a Gamma distribution that is conjugate to the posterior distribution.

Usage

DirichletProcessExponential(y, g0Priors = c(0.01, 0.01), alphaPriors = c(2, 4))

Arguments

Details

G_0(\theta | \alpha _0, \beta_0) = \mathrm{Gamma} \left(\theta | \alpha_0, \beta_0 \right)

Value

Dirichlet process object

Create a Dirichlet Mixture of Gaussians

Description

This is the constructor function to produce a dirichletprocess object with a Gaussian mixture kernel with unknown mean and variance. The base measure is a Normal Inverse Gamma distribution that is conjugate to the posterior distribution.

Usage

DirichletProcessGaussian(y, g0Priors = c(0, 1, 1, 1), alphaPriors = c(2, 4))

Arguments

Details

G_0(\theta | \gamma) = N \left(\mu | \mu_0, \frac{\sigma^2}{k_0} \right) \mathrm{Inv-Gamma} \left(\sigma^2 | \alpha_0, \beta_0 \right)

We recommend scaling your data to zero mean and unit variance for quicker convergence.

Value

Dirichlet process object

Create a Dirichlet Mixture of the Gaussian Distribution with fixed variance.

Description

Create a Dirichlet Mixture of the Gaussian Distribution with fixed variance.

Usage

DirichletProcessGaussianFixedVariance(
  y,
  sigma,
  g0Priors = c(0, 1),
  alphaPriors = c(2, 4)
)

Arguments

Value

Dirichlet process object

Create a Hierarchical Dirichlet Mixture of Beta Distributions

Description

Create a Hierarchical Dirichlet Mixture of Beta Distributions

Usage

DirichletProcessHierarchicalBeta(
  dataList,
  maxY,
  priorParameters = c(2, 8),
  hyperPriorParameters = c(1, 0.125),
  gammaPriors = c(2, 4),
  alphaPriors = c(2, 4),
  mhStepSize = c(0.1, 0.1),
  numSticks = 50,
  mhDraws = 250
)

Arguments

Value

dpobjlist A Hierarchical Dirichlet Process object that can be fitted, plotted etc.

Create a Hierarchical Dirichlet Mixture of semi-conjugate Multivariate Normal Distributions

Description

Create a Hierarchical Dirichlet Mixture of semi-conjugate Multivariate Normal Distributions

Usage

DirichletProcessHierarchicalMvnormal2(
  dataList,
  g0Priors,
  gammaPriors = c(2, 4),
  alphaPriors = c(2, 4),
  numSticks = 50,
  numInitialClusters = 1,
  mhDraws = 250
)

Arguments

Value

dpobjlist A Hierarchical Dirichlet Process object that can be fitted, plotted etc.

Create a Dirichlet mixture of multivariate normal distributions.

Description

G_0 (\boldsymbol{\mu} , \Lambda | \boldsymbol{\mu _0} , \kappa _0, \nu _0, T_0) = N ( \boldsymbol{\mu} | \boldsymbol{\mu _0} , (\kappa _0 \Lambda )^{-1} ) \mathrm{Wi} _{\nu _0} (\Lambda | T_0)

Usage

DirichletProcessMvnormal(
  y,
  g0Priors,
  alphaPriors = c(2, 4),
  numInitialClusters = 1
)

Arguments

Create a Dirichlet mixture of multivariate normal distributions with semi-conjugate prior.

Description

Create a Dirichlet mixture of multivariate normal distributions with semi-conjugate prior.

Usage

DirichletProcessMvnormal2(y, g0Priors, alphaPriors = c(2, 4))

Arguments

Create a Dirichlet Mixture of the Weibull distribution

Description

The likelihood is parameterised as \mathrm{Weibull} (y | a, b) = \frac{a}{b} y ^{a-1} \exp \left( - \frac{x^a}{b} \right). The base measure is a Uniform Inverse Gamma Distribution. G_0 (a, b | \phi, \alpha _0 , \beta _0) = U(a | 0, \phi ) \mathrm{Inv-Gamma} ( b | \alpha _0, \beta _0) \phi \sim \mathrm{Pareto}(x_m , k) \beta \sim \mathrm{Gamma} (\alpha _0 , \beta _0) This is a semi-conjugate distribution. The cluster parameter a is updated using the Metropolis Hastings algorithm an analytical posterior exists for b.

Usage

DirichletProcessWeibull(
  y,
  g0Priors,
  alphaPriors = c(2, 4),
  mhStepSize = c(1, 1),
  hyperPriorParameters = c(6, 2, 1, 0.5),
  verbose = FALSE,
  mhDraws = 250
)

Arguments

Value

Dirichlet process object

References

Kottas, A. (2006). Nonparametric Bayesian survival analysis using mixtures of Weibull distributions. Journal of Statistical Planning and Inference, 136(3), 578-596.

Create a Exponential mixing distribution

Description

See DirichletProcessExponential for details on the base measure.

Usage

ExponentialMixtureCreate(priorParameters = c(0.01, 0.01))

Arguments

Value

Mixing distribution object

Fit the Dirichlet process object

Description

Using Neal's algorithm 4 or 8 depending on conjugacy the sampling procedure for a Dirichlet process is carried out. Lists of both cluster parameters, weights and the sampled concentration values are included in the fitted dpObj. When update_prior is set to TRUE the parameters of the base measure are also updated.

Usage

Fit(dpObj, its, updatePrior = FALSE, progressBar = TRUE)

Arguments

Value

A Dirichlet Process object with the fitted cluster parameters and labels.

References

Neal, R. M. (2000). Markov chain sampling methods for Dirichlet process mixture models. Journal of computational and graphical statistics, 9(2), 249-265.

Fit a Hidden Markov Dirichlet Process Model

Description

Fit a Hidden Markov Dirichlet Process Model

Usage

## S3 method for class 'markov'
Fit(dpObj, its, updatePrior = F, progressBar = F)

Arguments

Value

A Dirichlet Process object with the fitted cluster parameters and states.

Create a Gaussian Mixing Distribution with fixed variance.

Description

Create a Gaussian Mixing Distribution with fixed variance.

Usage

GaussianFixedVarianceMixtureCreate(priorParameters = c(0, 1), sigma)

Arguments

Value

A mixing distribution object.

Create a Normal mixing distribution

Description

See DirichletProcessGaussian for details on the base measure.

Usage

GaussianMixtureCreate(priorParameters = c(0, 1, 1, 1))

Arguments

Value

Mixing distribution object

Update the parameters of the hierarchical Dirichlet process object.

Description

Update the parameters of the hierarchical Dirichlet process object.

Usage

GlobalParameterUpdate(dpobjlist)

Arguments

Create a Mixing Object for a hierarchical Beta Dirichlet process object.

Description

Create a Mixing Object for a hierarchical Beta Dirichlet process object.

Usage

HierarchicalBetaCreate(
  n,
  priorParameters,
  hyperPriorParameters,
  alphaPrior,
  maxT,
  gammaPrior,
  mhStepSize,
  num_sticks
)

Arguments

Value

A mixing distribution object.

Create a Mixing Object for a hierarchical semi-conjugate Multivariate Normal Dirichlet process object.

Description

Create a Mixing Object for a hierarchical semi-conjugate Multivariate Normal Dirichlet process object.

Usage

HierarchicalMvnormal2Create(
  n,
  priorParameters,
  alphaPrior,
  gammaPrior,
  num_sticks
)

Arguments

Value

A mixing distribution object.

Initialise a Dirichlet process object

Description

Initialise a Dirichlet process object by assigning all the data points to a single cluster with a posterior or prior draw for parameters.

Usage

Initialise(
  dpObj,
  posterior = TRUE,
  m = 3,
  verbose = TRUE,
  numInitialClusters = 1
)

Arguments

Value

A Dirichlet process object that has initial cluster allocations.

Mixing Distribution Likelihood

Description

Evaluate the Likelihood of some data x for some parameter \theta.

Usage

## S3 method for class 'beta'
Likelihood(mdObj, x, theta)

## S3 method for class 'beta2'
Likelihood(mdObj, x, theta)

## S3 method for class 'exponential'
Likelihood(mdObj, x, theta)

Likelihood(mdObj, x, theta)

## S3 method for class 'mvnormal'
Likelihood(mdObj, x, theta)

## S3 method for class 'mvnormal2'
Likelihood(mdObj, x, theta)

## S3 method for class 'normalFixedVariance'
Likelihood(mdObj, x, theta)

## S3 method for class 'normal'
Likelihood(mdObj, x, theta)

Arguments

Value

Likelihood of the data

The likelihood of the Dirichlet process object

Description

Calculate the likelihood of each data point with its parameter.

Usage

LikelihoodDP(dpobj)

Arguments

The Likelihood function of a Dirichlet process object.

Description

Collecting the fitted cluster parameters and number of datapoints associated with each parameter a likelihood can be calculated. Each cluster is weighted by the number of datapoints assigned.

Usage

LikelihoodFunction(dpobj, ind)

Arguments

Value

A function f(x) that represents the Likelihood of the dpobj.

Examples

y <- rnorm(10)
dp <- DirichletProcessGaussian(y)
dp <- Fit(dp, 5)
f <- LikelihoodFunction(dp)
plot(f(-2:2))

Create a mixing distribution object

Description

The constructor function for a mixing distribution object. Use this function to prepare an object for use with the appropriate distribution functions.

Usage

MixingDistribution(
  distribution,
  priorParameters,
  conjugate,
  mhStepSize = NULL,
  hyperPriorParameters = NULL
)

Arguments

Create a multivariate normal mixing distribution with semi conjugate prior

Description

Create a multivariate normal mixing distribution with semi conjugate prior

Usage

Mvnormal2Create(priorParameters)

Arguments

Create a multivariate normal mixing distribution

Description

Create a multivariate normal mixing distribution

Usage

MvnormalCreate(priorParameters)

Arguments

Calculate the parameters that maximise the penalised likelihood.

Description

Used to find suitable starting parameters for nonconjugate mixtures. For some mixing distributions this hasn't been implemented yet.

Usage

## S3 method for class 'beta'
PenalisedLikelihood(mdObj, x)

PenalisedLikelihood(mdObj, x)

## Default S3 method:
PenalisedLikelihood(mdObj, x)

Arguments

Generate the posterior clusters of a Dirichlet Process

Description

Using the stick breaking representation the user can draw the posterior clusters and weights for a fitted Dirichlet Process. See also PosteriorFunction.

Usage

PosteriorClusters(dpobj, ind)

Arguments

Value

A list with the weights and cluster parameters that form the posterior of the Dirichlet process.

Examples

y <- rnorm(10)
dp <- DirichletProcessGaussian(y)
dp <- Fit(dp, 5)
postClusters <- PosteriorClusters(dp)

Draw from the posterior distribution

Description

Draw from the posterior distribution

Usage

## S3 method for class 'exponential'
PosteriorDraw(mdObj, x, n = 1, ...)

PosteriorDraw(mdObj, x, n = 1, ...)

## S3 method for class 'mvnormal'
PosteriorDraw(mdObj, x, n = 1, ...)

## S3 method for class 'mvnormal2'
PosteriorDraw(mdObj, x, n = 1, ...)

## S3 method for class 'normalFixedVariance'
PosteriorDraw(mdObj, x, n = 1, ...)

## S3 method for class 'normal'
PosteriorDraw(mdObj, x, n = 1, ...)

## S3 method for class 'weibull'
PosteriorDraw(mdObj, x, n = 100, ...)

Arguments

Value

A sample from the posterior distribution

Calculate the posterior mean and quantiles from a Dirichlet process object.

Description

Calculate the posterior mean and quantiles from a Dirichlet process object.

Usage

PosteriorFrame(dpobj, xgrid, ndraws = 1000, ci_size = 0.1)

Arguments

Value

A dataframe consisting of the posterior mean and credible intervals.

Generate the posterior function of the Dirichlet function

Description

Generate the posterior function of the Dirichlet function

Usage

PosteriorFunction(dpobj, ind)

Arguments

Value

A posterior function f(x).

Examples

y <- rnorm(10)
dp <- DirichletProcessGaussian(y)
dp <- Fit(dp, 5)
postFuncDraw <- PosteriorFunction(dp)
plot(-3:3, postFuncDraw(-3:3))

Calculate the posterior parameters for a conjugate prior.

Description

Calculate the posterior parameters for a conjugate prior.

Usage

PosteriorParameters(mdObj, x)

## S3 method for class 'mvnormal'
PosteriorParameters(mdObj, x)

## S3 method for class 'normalFixedVariance'
PosteriorParameters(mdObj, x)

## S3 method for class 'normal'
PosteriorParameters(mdObj, x)

Arguments

Value

Parameters of the posterior distribution

Calculate how well the prior predicts the data.

Description

Calculate how well the prior predicts the data.

Usage

## S3 method for class 'exponential'
Predictive(mdObj, x)

Predictive(mdObj, x)

## S3 method for class 'mvnormal'
Predictive(mdObj, x)

## S3 method for class 'normalFixedVariance'
Predictive(mdObj, x)

## S3 method for class 'normal'
Predictive(mdObj, x)

Arguments

Value

The probability of the data being from the prior.

Draw prior clusters and weights from the Dirichlet process

Description

Draw prior clusters and weights from the Dirichlet process

Usage

PriorClusters(dpobj)

Arguments

Value

A list of weights and parameters of the prior distribution of the Dirichcet process

Calculate the prior density of a mixing distribution

Description

Calculate the prior density of a mixing distribution

Usage

## S3 method for class 'beta'
PriorDensity(mdObj, theta)

## S3 method for class 'beta2'
PriorDensity(mdObj, theta)

PriorDensity(mdObj, theta)

## S3 method for class 'weibull'
PriorDensity(mdObj, theta)

Arguments

Draw from the prior distribution

Description

Draw from the prior distribution

Usage

## S3 method for class 'beta'
PriorDraw(mdObj, n = 1)

## S3 method for class 'beta2'
PriorDraw(mdObj, n = 1)

## S3 method for class 'exponential'
PriorDraw(mdObj, n)

PriorDraw(mdObj, n)

## S3 method for class 'mvnormal'
PriorDraw(mdObj, n = 1)

## S3 method for class 'mvnormal2'
PriorDraw(mdObj, n = 1)

## S3 method for class 'normalFixedVariance'
PriorDraw(mdObj, n = 1)

## S3 method for class 'normal'
PriorDraw(mdObj, n = 1)

## S3 method for class 'weibull'
PriorDraw(mdObj, n = 1)

Arguments

Value

A sample from the prior distribution

Generate the prior function of the Dirichlet process

Description

Generate the prior function of the Dirichlet process

Usage

PriorFunction(dpobj)

Arguments

Value

A function f(x) that represents a draw from the prior distrubtion of the Dirichlet process.

@export

Update the prior parameters of a mixing distribution

Description

Update the prior parameters of a mixing distribution

Usage

## S3 method for class 'beta'
PriorParametersUpdate(mdObj, clusterParameters, n = 1)

PriorParametersUpdate(mdObj, clusterParameters, n = 1)

## S3 method for class 'weibull'
PriorParametersUpdate(mdObj, clusterParameters, n = 1)

Arguments

Value

mdobj New Mixing Distribution object with updated cluster parameters

The Stick Breaking representation of the Dirichlet process.

Description

A Dirichlet process can be represented using a stick breaking construction

G = \sum _{i=1} ^n pi _i \delta _{\theta _i}

, where \pi _k = \beta _k \prod _{k=1} ^{n-1} (1- \beta _k ) are the stick breaking weights. The atoms \delta _{\theta _i} are drawn from G_0 the base measure of the Dirichlet Process. The \beta _k \sim \mathrm{Beta} (1, \alpha). In theory n should be infinite, but we chose some value of N to truncate the series. For more details see reference.

Usage

StickBreaking(alpha, N)

piDirichlet(betas)

Arguments

Value

Vector of stick breaking probabilities.

Functions

• piDirichlet(): Function for calculating stick lengths.

References

Ishwaran, H., & James, L. F. (2001). Gibbs sampling methods for stick-breaking priors. Journal of the American Statistical Association, 96(453), 161-173.

Update the Dirichlet process concentration parameter.

Description

Using the procedure outlined in West (1992) we sample the concentration parameter of the Dirichlet process. See reference for further details.

Usage

UpdateAlpha(dpobj)

## Default S3 method:
UpdateAlpha(dpobj)

## S3 method for class 'hierarchical'
UpdateAlpha(dpobj)

Arguments

Value

A Dirichlet process object with updated concentration parameter.

References

West, M. (1992). Hyperparameter estimation in Dirichlet process mixture models. ISDS Discussion Paper# 92-A03: Duke University.

Update the \alpha and \beta parameter of a hidden Markov Dirichlet process model.

Description

Update the \alpha and \beta parameter of a hidden Markov Dirichlet process model.

Usage

UpdateAlphaBeta(dp)

Arguments

Create a Weibull mixing distribution.

Description

See DirichletProcessWeibull for the default prior and hyper prior distributions.

Usage

WeibullMixtureCreate(
  priorParameters,
  mhStepSize,
  hyperPriorParameters = c(6, 2, 1, 0.5)
)

Arguments

Value

A mixing distribution object.

A flexible package for fitting Bayesian non-parametric models.

Description

Create, fit and take posterior samples from a Dirichlet process.

Plot the Dirichlet process object

Description

For a univariate Dirichlet process plot the density of the data with the posterior distribution and credible intervals overlayed. For multivariate data the first two columns of the data are plotted with the data points coloured by their cluster labels. The additional arguments are not used for multivariate data.

Usage

## S3 method for class 'dirichletprocess'
plot(x, ...)

plot_dirichletprocess_univariate(
  x,
  likelihood = FALSE,
  single = TRUE,
  data_fill = "black",
  data_method = "density",
  data_bw = NULL,
  ci_size = 0.05,
  xgrid_pts = 100,
  quant_pts = 100,
  xlim = NA
)

plot_dirichletprocess_multivariate(x)

Arguments

Value

A ggplot object.

Examples

dp <- DirichletProcessGaussian(c(rnorm(50, 2, .2), rnorm(60)))
dp <- Fit(dp, 100)
plot(dp)

plot(dp, likelihood = TRUE, data_method = "hist",
     data_fill = rgb(.5, .5, .8, .6), data_bw = .3)

Print the Dirichlet process object

Description

Print a Dirichlet process object. This will print some basic information about the dirichletprocess object.

Usage

## S3 method for class 'dirichletprocess'
print(x, param_summary = FALSE, digits = 2, ...)

Arguments

Examples

dp <- Fit(DirichletProcessGaussian(rnorm(10)), 100)
dp

Tumour incidences in rats

Description

Rat tumour data from Tarone (1982). Data from Table 5.1 of Bayesian Data Analysis

Usage

rats

Format

y

number of rats with a tumour

N

total number of rats in the experiment

Source

http://www.stat.columbia.edu/~gelman/book/data/rats.asc

Identifies the correct clusters labels, in any dimension, when cluster parameters and global parameters are matched.

Description

Identifies the correct clusters labels, in any dimension, when cluster parameters and global parameters are matched.

Usage

true_cluster_labels(array, dpObj)

Arguments

Value

The array containing the correct matching indexes

Generate a weighted function.

Description

Generate a weighted function.

Usage

weighted_function_generator(func, weights, params)

Arguments

Value

weighted function