Package 'AntMAN'

Description

Given an AM_mcmc_output object, this function returns the clustering matrix.

Usage

AM_clustering(fit)

Arguments

Details

The clustering matrix is an M by n matrix. Each of the M rows represents a clustering of n items using cluster labels. Items i and j are in the same cluster if fit[m,i] == fit[m,j] for the mth clustering.

Value

A numeric clustering matrix

See Also

AM_coclustering

Examples

fit = AM_demo_uvp_poi()$fit
ccm <- AM_clustering(fit)

Description

Given an AM_mcmc_output object, this function returns the co-clustering matrix.

Usage

AM_coclustering(fit)

Arguments

Details

The co-clustering matrix is produced by the simultaneous clustering of the rows and columns. Each entry denotes the (posterior) probability that items $i$ and $j$ are together. This technique is also known as bi-clustering and block clustering (Govaert and Nadif 2013), and is useful for understanding the number of clusters in the dataset.

Value

A numeric co-clustering matrix

See Also

AM_clustering

Examples

fit = AM_demo_uvp_poi()$fit
ccm <- AM_coclustering(fit)

Returns an example of AM_mcmc_fit output produced by the multivariate bernoulli model

Description

This function allows us to generate a sample output of fitting the multivariate Bernoulli model. No arguments are needed to be passed. The purpose of this function is to serve as a demo for users to understand the model's output, without diving too deep into details. By default, this demo generates a sample dataset of dimension 500x4, where the MCMC sampler is specified to run for 2000 iterations, with a burn-in of 1000, and a thinning interval of 10. All possible outputs that can be produced by AM_mcmc_fit are returned (see return value below).

Usage

AM_demo_mvb_poi()

Value

A list containing the following items:

• the vector (or matrix) containing the synthetic data used to fit the model.

• the vector containing the final cluster assignment of each observation.

• an AM_mcmc_output object, which is the typical output of AM_mcmc_fit.

Examples

mvb_output <- AM_demo_mvb_poi()

Returns an example of AM_mcmc_fit output produced by the multivariate gaussian model

Description

This function allows us to generate a sample output of fitting the multivariate Gaussian model. No arguments are needed to be passed. The purpose of this function is to serve as a demo for users to understand the model's output, without diving too deep into details. By default, this demo generates a sample dataset of dimension 500x2, where the MCMC sampler is specified to run for 2000 iterations, with a burn-in of 1000, and a thinning interval of 10. All possible outputs that can be produced by AM_mcmc_fit are returned (see return value below).

Usage

AM_demo_mvn_poi()

Value

A list containing the following items:

• the vector (or matrix) containing the synthetic data used to fit the model.

• the vector containing the final cluster assignment of each observation.

• an AM_mcmc_output object, which is the typical output of AM_mcmc_fit.

Examples

mvn_output <- AM_demo_mvn_poi()

Returns an example of AM_mcmc_fit output produced by the univariate Gaussian model

Description

This function allows us to generate a sample output of fitting the univariate gaussian model. No arguments are needed to be passed. The purpose of this function is to serve as a demo for users to understand the model's output, without diving too deep into details. By default, this demo generates a sample dataset of dimension 500x1, where the MCMC sampler is specified to run for 2000 iterations, with a burn-in of 1000, and a thinning interval of 10. All possible outputs that can be produced by AM_mcmc_fit are returned (see return value below).

Usage

AM_demo_uvn_poi()

Value

A list containing the following items:

• the vector (or matrix) containing the synthetic data used to fit the model.

• the vector containing the final cluster assignment of each observation.

• an AM_mcmc_output object, which is the typical output of AM_mcmc_fit.

Examples

mvn_output <- AM_demo_uvn_poi()

Returns an example of AM_mcmc_fit output produced by the univariate Poisson model

Description

This function allows us to generate a sample output of fitting the univariate poisson model. No arguments are needed to be passed. The purpose of this function is to serve as a demo for users to understand the model's output, without diving too deep into details. By default, this demo generates a sample dataset of dimension 500x1, where the MCMC sampler is specified to run for 2000 iterations, with a burn-in of 1000, and a thinning interval of 10. All possible outputs that can be produced by AM_mcmc_fit are returned (see return value below).

Usage

AM_demo_uvp_poi()

Value

A list containing the following items:

• the vector (or matrix) containing the synthetic data used to fit the model.

• the vector containing the final cluster assignment of each observation.

• an AM_mcmc_output object, which is the typical output of AM_mcmc_fit.

Examples

mvn_output <- AM_demo_uvn_poi()

Description

This function computes the hyperparameters of a Normal Inverse-Gamma distribution using an empirical Bayes approach. More information about how these hyperparameters are determined can be found here: Bayes and empirical Bayes: do they merge? (Petrone et al. 2012).

Usage

AM_emp_bayes_uninorm(y, scEmu = 1, scEsig2 = 3, CVsig2 = 3)

Arguments

Value

an object of class AM_mix_hyperparams, in which hyperparameters m0, k0, nu0 and sig02 are specified. To understand the usage of these hyperparameters, please refer to AM_mix_hyperparams_uninorm.

Extract values within a AM_mcmc_output object

Description

Given an AM_mcmc_output object, as well as the target variable names, AM_extract will return a list of the variables of interest.

Usage

AM_extract(object, targets, iterations = NULL, debug = FALSE)

Arguments

Details

Due to the complexity of AntMAN outputs, AM_mcmc_output object can be difficult to handle. The AM_extract function eases access of particular variables within the AM_mcmc_output object. Variables of varying dimension are expected to result from the transdimensional moves. When considering such variables, the extracted list would correspond to an nx1 list, where n refers to the number of extracted iterations. Each of these nx1 entries consists of another list of dimension mx1, where m specifies the number of components inferred for that iteration.

Value

a list of variables specified in targets.

Description

Once a fixed value of the number of components $M^*$ is specified, this function adopts a bisection method to find the value of $\gamma$ such that the induced distribution on the number of clusters is centered around a user specifed value $K^*$, i.e. the function uses a bisection method to solve for $\gamma$ (Argiento and Iorio 2019). The user can provide a lower $\gamma_{l}$ and an upper $\gamma_{u}$ bound for the possible values of $\gamma$. The default values are $\gamma_l= 10^{-3}$ and $\gamma_{u}=10$. A default value for the tolerance is $\epsilon=0.1$. Moreover, after a maximum number of iteration (default is 31), the function stops warning that convergence has not been reached.

Usage

AM_find_gamma_Delta(
  n,
  Mstar,
  Kstar = 6,
  gam_min = 1e-04,
  gam_max = 10,
  tolerance = 0.1
)

Arguments

Value

A value of gamma such that $E(K)=K^*$

Examples

n <- 82
Mstar <- 12
gam_de <- AM_find_gamma_Delta(n,Mstar,Kstar=6, gam_min=1e-4,gam_max=10, tolerance=0.1)
prior_K_de <-  AM_prior_K_Delta(n,gam_de,Mstar)
prior_K_de%*%1:n

Description

Once the prior on the number of mixture components M is assumed to be a Negative Binomial with parameter r>0 and 0<p<1, with mean is 1+ r*p/(1-p), this function adopts a bisection method to find the value of gamma such that the induced distribution on the number of clusters is centered around a user specifed value $K^{*}$, i.e. the function uses a bisection method to solve for $\gamma$ (Argiento and Iorio 2019). The user can provide a lower $\gamma_{l}$ and an upper $\gamma_{u}$ bound for the possible values of $\gamma$. The default values are $\gamma_l= 10^{-3}$ and $\gamma_{u}=10$. A defaault value for the tolerance is $\epsilon=0.1$. Moreover, after a maximum number of iteration (default is 31), the function stops warning that convergence has not bee reached.

Usage

AM_find_gamma_NegBin(
  n,
  r,
  p,
  Kstar = 6,
  gam_min = 0.001,
  gam_max = 10000,
  tolerance = 0.1
)

Arguments

Value

A value of gamma such that $E(K)=K^{*}$

Examples

n <- 82
r <- 1
p <- 0.8571
gam_nb= AM_find_gamma_NegBin(n,r,p,Kstar=6, gam_min=0.001,gam_max=10000, tolerance=0.1)
prior_K_nb= AM_prior_K_NegBin(n,gam_nb, r, p)
prior_K_nb%*%1:n

Description

Once the prior on the number of mixture components M is assumed to be a Shifted Poisson of parameter Lambda, this function adopts a bisection method to find the value of $\gamma$ such that the induced distribution on the number of clusters is centered around a user specifed value $K^{*}$, i.e. the function uses a bisection method to solve for $\gamma$ (Argiento and Iorio 2019). The user can provide a lower $\gamma_{l}$ and an upper $\gamma_{u}$ bound for the possible values of $\gamma$. The default values are $\gamma_l= 10^{-3}$ and $\gamma_{u}=10$. A defaault value for the tolerance is $\epsilon=0.1$. Moreover, after a maximum number of iteration (default is 31), the function stops warning that convergence has not bee reached.

Usage

AM_find_gamma_Pois(
  n,
  Lambda,
  Kstar = 6,
  gam_min = 1e-04,
  gam_max = 10,
  tolerance = 0.1
)

Arguments

Value

A value of gamma such that $E(K)=K^{*}$

Examples

n <- 82
Lam  <- 11
gam_po <-  AM_find_gamma_Pois(n,Lam,Kstar=6, gam_min=0.0001,gam_max=10, tolerance=0.1)
prior_K_po <-  AM_prior_K_Pois(n,gam_po,Lam)
prior_K_po%*%1:n

Description

Output type of return values from AM_mcmc_parameters.

Value

AM_mcmc_configuration

See Also

AM_mcmc_fit

Description

The AM_mcmc_fit function performs a Gibbs sampling in order to estimate the mixture comprising the sample data y. The mixture selected must be of a predefined type mix_kernel_hyperparams (defined with AM_mix_hyperparams_* functions, where star * denotes the chosen kernel). Additionally, a prior distribution on the number of mixture components must be specified through mix_components_prior (generated with AM_mix_components_prior_* functions, where * denotes the chosen prior). Similarly, a prior on the weights of the mixture should be specified through mix_weight_prior (defined with AM_mix_weights_prior_* functions). Finally, with mcmc_parameters, the user sets the MCMC parameters for the Gibbs sampler (defined with AM_mcmc_parameters functions).

Usage

AM_mcmc_fit(
  y,
  mix_kernel_hyperparams,
  initial_clustering = NULL,
  init_K = NULL,
  fixed_clustering = NULL,
  mix_components_prior = AM_mix_components_prior_pois(),
  mix_weight_prior = AM_mix_weights_prior_gamma(),
  mcmc_parameters = AM_mcmc_parameters()
)

Arguments

Details

If no initial clustering is specified (either as init_K or init_clustering), then every observation is allocated to a different cluster. If init_K is specified then AntMAN initialises the clustering through K-means.

Warning: if the user does not specify init_K or initial_cluster, the first steps can be be time-consuming because of default setting of the initial clustering.

Value

The return value is an AM_mcmc_output object.

Examples

AM_mcmc_fit( AM_sample_unipois()$y, 
              AM_mix_hyperparams_unipois (alpha0=2, beta0=0.2), 
              mcmc_parameters = AM_mcmc_parameters(niter=50, burnin=0, thin=1, verbose=0))

Description

Output type of return values from AM_mcmc_fit.

Value

AM_mcmc_output

See Also

AM_mcmc_fit

Description

This function generates an MCMC parameters list to be used as mcmc_parameters argument within AM_mcmc_fit.

Usage

AM_mcmc_parameters(
  niter = 5000,
  burnin = 2500,
  thin = 1,
  verbose = 1,
  output = c("CI", "K"),
  parallel = TRUE,
  output_dir = NULL
)

Arguments

Value

An AM_mcmc_configuration Object. This is a list to be used as mcmc_parameters argument with AM_mcmc_fit.

Examples

AM_mcmc_parameters (niter=1000, burnin=10000, thin=50)
AM_mcmc_parameters (niter=1000, burnin=10000, thin=50, output=c("CI","W","TAU"))

Description

Similar to AM_mcmc_fit, the AM_mcmc_refit function performs a Gibbs sampling in order to estimate a mixture. However parameters will be reused from a previous result from AM_mcmc_fit.

Usage

AM_mcmc_refit(y, fit, fixed_clustering, mcmc_parameters = AM_mcmc_parameters())

Arguments

Details

In practice this function will call AM_mcmc_fit(y, fixed_clustering = fixed_clustering, ...); with the same parameters as previously specified.

Value

The return value is an AM_mcmc_output object.

Examples

y = AM_sample_unipois()$y
 fit = AM_mcmc_fit( y , 
              AM_mix_hyperparams_unipois (alpha0=2, beta0=0.2), 
              mcmc_parameters = AM_mcmc_parameters(niter=20, burnin=0, thin=1, verbose=0))
 eam = AM_coclustering(fit)
 cluster = AM_salso(eam, "binder")
 refit = AM_mcmc_refit(y , fit, cluster, 
         mcmc_parameters = AM_mcmc_parameters(niter=20, burnin=0, thin=1, verbose=0));

Description

Object returned by AM_mix_components_prior_*.

Value

AM_mix_components_prior

See Also

AM_mix_components_prior_dirac, AM_mix_components_prior_negbin, AM_mix_components_prior_pois

Description

Generate a configuration object that assigns a Point mass prior to the number of mixture components. This is the simplest option and it requires users to specify a value $M^*$ such that $Pr(M=M^* =1$.

Usage

AM_mix_components_prior_dirac(Mstar)

Arguments

Value

An AM_mix_components_prior object. This is a configuration list to be used as mix_components_prior argument for AM_mcmc_fit.

See Also

AM_mcmc_fit

Examples

AM_mix_components_prior_dirac (Mstar=3)

Description

This generates a configuration object for a Shifted Negative Binomial prior on the number of mixture components such that

$q_M(m)=Pr(M=m) =\frac{\Gamma(r+m-1)}{(m-1)!\Gamma(r)} p^{m-1}(1-p)^r, \quad m=1,2,3,\ldots$

The hyperparameters $p\in (0,1)$ (probability of success) and $r>0$ (size) can either be fixed using r and p or assigned appropriate prior distributions. In the latter case, we assume $p \sim Beta(a_P,b_P)$ and $r \sim Gamma(a_R,b_R)$. In AntMAN we assume the following parametrization of the Gamma density:

$p(x\mid a,b )= \frac{b^a x^{a-1}}{\Gamma(a)} \exp\{ -bx \}, \quad x>0.$

Usage

AM_mix_components_prior_negbin(
  a_R = NULL,
  b_R = NULL,
  a_P = NULL,
  b_P = NULL,
  R = NULL,
  P = NULL,
  init_R = NULL,
  init_P = NULL
)

Arguments

Details

If no arguments are provided, the default is $r = 1 , a_P = 1, b_P = 1$.

Additionally, when init_R and init_P are not specified, there are default values: $init_R = 1$ and $init_P = 0.5$.

Value

An AM_mix_components_prior object. This is a configuration list to be used as mix_components_prior argument for AM_mcmc_fit.

See Also

AM_mcmc_fit

Examples

AM_mix_components_prior_negbin (R=1, P=1)
AM_mix_components_prior_negbin ()

Description

This function generates a configuration object for a Shifted Poisson prior on the number of mixture components such that

$q_M(m)= Pr (M=m)= \frac{e^{-\Lambda}\Lambda^{m-1} }{(m-1)!} , \quad m=1,2,3,\ldots$

The hyperparameter $\Lambda$ can either be fixed using Lambda or assigned a $Gamma(a,b)$ prior distribution with a and b. In AntMAN we assume the following parametrization of the Gamma density:

$p(x\mid a,b )= \frac{b^a x^{a-1}}{\Gamma(a)} \exp\{ -bx \}, \quad x>0.$

Usage

AM_mix_components_prior_pois(a = NULL, b = NULL, Lambda = NULL, init = NULL)

Arguments

Details

If no arguments are provided, the default is a prior distribution with a = 1 and b = 1.

Value

An AM_mix_components_prior object. This is a configuration list to be used as mix_components_prior argument for AM_mcmc_fit.

See Also

AM_mcmc_fit

Examples

components_prior = AM_mix_components_prior_pois (init=3,  a=1, b=1)

Description

Object type returned by AM_mix_hyperparams_* commands.

Value

AM_mix_hyperparams

See Also

AM_mix_hyperparams_unipois, AM_mix_hyperparams_uninorm, AM_mix_hyperparams_multiber, AM_mix_hyperparams_multinorm

Description

Generate a configuration object that defines the prior hyperparameters for a mixture of multivariate Bernoulli. If the dimension of the data is P, then the prior is a product of P independent Beta distributions, Beta($a_{0i},b_{0i}$). Therefore, the vectors of hyperparameters, a0 and b0, are P-dimensional. Default is (a0= c(1,....,1),b0= c(1,....,1)).

Usage

AM_mix_hyperparams_multiber(a0, b0)

Arguments

Value

An AM_mix_hyperparams object. This is a configuration list to be used as mix_kernel_hyperparams argument for AM_mcmc_fit.

Examples

AM_mix_hyperparams_multiber (a0= c(1,1,1,1),b0= c(1,1,1,1))

Description

Generate a configuration object that specifies a multivariate Normal mixture kernel, where users can specify the hyperparameters for the conjugate prior of the multivariate Normal mixture. We assume that the data are d-dimensional vectors $\boldsymbol{y}_i$, where $\boldsymbol{y}_i$ are i.i.d Normal random variables with mean $\boldsymbol{\mu}$ and covariance matrix $\boldsymbol{\Sigma}$. The conjugate prior is

$\pi(\boldsymbol \mu, \boldsymbol \Sigma\mid\boldsymbol m_0,\kappa_0,\nu_0,\boldsymbol \Lambda_0)= \pi_{\mu}(\boldsymbol \mu|\boldsymbol \Sigma,\boldsymbol m_0,\kappa_0)\pi_{\Sigma}(\boldsymbol \Sigma \mid \nu_0,\boldsymbol \Lambda_0),$

$\pi_{\mu}(\boldsymbol \mu|\boldsymbol \Sigma,\boldsymbol m_0,\kappa_0) = \frac{\sqrt{\kappa_0^d}}{\sqrt {(2\pi )^{d}|{\boldsymbol \Sigma }|}} \exp \left(-{\frac {\kappa_0}{2}}(\boldsymbol\mu -{\boldsymbol m_0 })^{\mathrm {T} }{\boldsymbol{\Sigma }}^{-1}(\boldsymbol\mu-{\boldsymbol m_0 })\right), \qquad \boldsymbol \mu\in\mathcal{R}^d,$

$\pi_{\Sigma}(\boldsymbol \Sigma\mid \nu_0,\boldsymbol \Lambda_0)= {\frac {\left|{\boldsymbol \Lambda_0 }\right|^{\nu_0 /2}}{2^{\nu_0 d/2}\Gamma _{d}({\frac {\nu_0 }{2}})}}\left|\boldsymbol \Sigma \right|^{-(\nu_0 +d+1)/2}e^{-{\frac {1}{2}}\mathrm {tr} (\boldsymbol \Lambda_0 \boldsymbol \Sigma^{-1})} , \qquad \boldsymbol \Sigma^2>0,$

where mu0 corresponds to $\boldsymbol m_0$, ka0 corresponds to $\kappa_0$, nu0 to $\nu_0$, and Lam0 to $\Lambda_0$.

Usage

AM_mix_hyperparams_multinorm(mu0 = NULL, ka0 = NULL, nu0 = NULL, Lam0 = NULL)

Arguments

Details

Default is (mu0=c(0,..,0), ka0=1, nu0=Dim+2, Lam0=diag(Dim)) with Dim is the dimension of the data y. We advise the user to set $\nu_0$ equal to at least the dimension of the data, Dim, plus 2.

Value

An AM_mix_hyperparams object. This is a configuration list to be used as mix_kernel_hyperparams argument for AM_mcmc_fit.

Examples

AM_mix_hyperparams_multinorm ()

Description

Generate a configuration object that specifies a univariate Normal mixture kernel, where users can specify the hyperparameters of the Normal-InverseGamma conjugate prior. As such, the kernel is a Gaussian distribution with mean $\mu$ and variance $\sigma^2$. The prior on $(\mu,\sigma^2)$ the Normal-InverseGamma:

$\pi(\mu,\sigma^2\mid m_0,\kappa_0,\nu_0,\sigma^2_0) = \pi_{\mu}(\mu|\sigma^2,m_0,\kappa_0)\pi_{\sigma^2}(\sigma^2\mid \nu_0,\sigma^2_0),$

$\pi_{\mu}(\mu|\sigma^2,m_0,\kappa_0) =\frac{\sqrt{\kappa_0}}{\sqrt{2\pi\sigma^2},} \exp^{-\frac{\kappa_0}{2\sigma^2}(\mu-m_0)^2 }, \qquad \mu\in\mathcal{R},$

$\pi_{\sigma^2}(\sigma^2\mid \nu_0,\sigma^2_0)= {\frac {\sigma_0^{2^{\nu_0 }}}{\Gamma (\nu_0 )}}(1/\sigma^2)^{\nu_0 +1}\exp \left(-\frac{\sigma_0^2}{\sigma^2}\right), \qquad \sigma^2>0.$

Usage

AM_mix_hyperparams_uninorm(m0, k0, nu0, sig02)

Arguments

Details

$m_0$ corresponds m0, $\kappa_0$ corresponds k0, $\nu_0$ corresponds nu0, and $\sigma^2_0$ corresponds sig02.

If hyperparameters are not specified, the default is m0=0, k0=1, nu0=3, sig02=1.

Value

An AM_mix_hyperparams object. This is a configuration list to be used as mix_kernel_hyperparams argument for AM_mcmc_fit.

Examples

#### This example ...
     
     data(galaxy)
     y_uvn = galaxy
     mixture_uvn_params = AM_mix_hyperparams_uninorm  (m0=20.83146, k0=0.3333333,
                                                       nu0=4.222222, sig02=3.661027)
     
     mcmc_params        = AM_mcmc_parameters(niter=2000, burnin=500, thin=10, verbose=0)
     components_prior   = AM_mix_components_prior_pois (init=3,  a=1, b=1) 
     weights_prior      = AM_mix_weights_prior_gamma(init=2, a=1, b=1)
     
     fit <- AM_mcmc_fit(
       y = y_uvn,
       mix_kernel_hyperparams = mixture_uvn_params,
       mix_components_prior =components_prior,
       mix_weight_prior = weights_prior,
       mcmc_parameters = mcmc_params)
     
     summary (fit)
     plot (fit)

Description

Generate a configuration object that specifies a univariate Poisson mixture kernel, where users can specify the hyperparameters of the conjugate Gamma prior, i.e. the kernel is a $Poisson(\tau)$ and $\tau\sim Gamma(\alpha_0,\beta_0)$. In AntMAN we assume the following parametrization of the Gamma density:

$p(x\mid a,b )= \frac{b^a x^{a-1}}{\Gamma(a)} \exp\{ -bx \}, \quad x>0.$

Usage

AM_mix_hyperparams_unipois(alpha0, beta0)

Arguments

Details

Note that by default, alpha0=1 and beta0=1.

Value

An AM_mix_hyperparams object. This is a configuration list to be used as mix_kernel_hyperparams argument for AM_mcmc_fit.

Examples

AM_mix_hyperparams_unipois (alpha0=2, beta0=0.2)

Description

Object type returned by AM_mix_weights_prior_* commands.

Value

AM_mix_weights_prior

See Also

AM_mix_weights_prior_gamma

Description

Generate a configuration object to specify a prior on the hyperparameter $\gamma$ for the Dirichlet prior on the mixture weights. We assume $\gamma \sim Gamma(a,b)$. Alternatively, we can fix $\gamma$ to a specific value. Default is $\gamma=1/N$, where N is the number of observations. In AntMAN we assume the following parametrization of the Gamma density:

$p(x\mid a,b )= \frac{b^a x^{a-1}}{\Gamma(a)} \exp\{ -bx \}, \quad x>0.$

Usage

AM_mix_weights_prior_gamma(a = NULL, b = NULL, gamma = NULL, init = NULL)

Arguments

Value

A AM_mix_weights_prior object. This is a configuration list to be used as mix_weight_prior argument for AM_mcmc_fit.

Examples

AM_mix_weights_prior_gamma (a=1, b=1)
AM_mix_weights_prior_gamma (a=1, b=1, init=1)
AM_mix_weights_prior_gamma (gamma = 3)
AM_mix_weights_prior_gamma ()

Description

Given an AM_mcmc_output object, this function produces the autocorrelation function bars describing the MCMC results. AM_plot_chaincor makes use of bayesplot’s plotting function mcmc_acf_bar (Gabry et al. 2019).

Usage

AM_plot_chaincor(x, tags = NULL, lags = NULL, title = "MCMC Results")

Arguments

Value

A ggplot object.

Plot the density of variables from AM_mcmc_output object

Description

Given an AM_mcmc_output object, AM_plot_density plots the posterior density of the specified variables of interest. AM_plot_density makes use of bayesplot's plotting function mcmc_areas (Gabry et al. 2019).

Usage

AM_plot_density(x, tags = NULL, title = "MCMC Results")

Arguments

Value

a ggplot object visualising the posterior density of the specified variables.

Description

Given an AM_mcmc_output object, and the data the model was fit on, this function will produce a cluster frequency plot for the multivariate bernoulli model.

Usage

AM_plot_mvb_cluster_frequency(
  fit,
  y,
  x_lim_param = c(0.8, 7.2),
  y_lim_param = c(0, 1)
)

Arguments

Value

No return value. Called for side effects.

Plot AM_mcmc_output scatterplot matrix

Description

visualise a matrix of plots describing the MCMC results. This function is built upon GGally's plotting function ggpairs (Schloerke et al. 2021).

Usage

AM_plot_pairs(x, tags = NULL, title = "MCMC Results")

Arguments

Value

Same as ggpairs function, a ggmatrix object that if called, will print.

Plot the probability mass function of variables from AM_mcmc_output object

Description

Given an AM_mcmc_output object, AM_plot_pmf plots the posterior probability mass function of the specified variables.

Usage

AM_plot_pmf(x, tags = NULL, title = "MCMC Results")

Arguments

Value

No return value. Called for side effects.

Description

Given an AM_mcmc_output object, this function will produce an image of the Similarity Matrix.

Usage

AM_plot_similarity_matrix(x, loss, ...)

Arguments

Value

No return value. Called for side effects.

Plot traces of variables from an AM_mcmc_output object

Description

Given an AM_mcmc_output object, AM_plot_traces visualises the traceplots of the specified variables involved in the MCMC inference. AM_plot_traces is built upon bayesplot's mcmc_trace (Gabry et al. 2019).

Usage

AM_plot_traces(x, tags = NULL, title = "MCMC Results")

Arguments

Value

No return value. Called for side effects.

Description

Given an object of class AM_mcmc_fit, AM_plot_values visualises the interval estimates of the specified variables involved in the MCMC inference. AM_plot_values is built upon bayesplot's mcmc_intervals (Gabry et al. 2019).

Usage

AM_plot_values(x, tags = NULL, title = "MCMC Results")

Arguments

Value

No return value. Called for side effects.

Description

Object type returned by AM_prior_* commands.

Value

AM_prior

See Also

AM_prior_K_Delta, AM_prior_K_Pois, AM_prior_K_NegBin

Description

This function computes the prior on the number of clusters, i.e. occupied components of the mixture for a Finite Dirichlet process when the prior on the component-weights of the mixture is a Dirichlet with parameter gamma (i.e. when unnormalised weights are distributed as Gamma($\gamma$,1)). This function can be used when the number of components is fixed to $M^*$, i.e. a Dirac prior assigning mass only to $M^*$ is assumed. See (Argiento and Iorio 2019) There are no default values.

Usage

AM_prior_K_Delta(n, gamma, Mstar)

Arguments

Value

an AM_prior object, that is a vector of length n, reporting the values V(n,k) for k=1,...,n.

Examples

n <- 82
gam_de <- 0.1743555
Mstar <- 12
prior_K_de <- AM_prior_K_Delta(n,gam_de, Mstar)
plot(prior_K_de)

Description

This function computes the prior on the number of clusters, i.e. occupied component of the mixture for a Finite Dirichlet process when the prior on the component-weights of the mixture is a Dirichlet with parameter gamma (i.e. when unnormalized weights are distributed as Gamma($\gamma$,1)). This function can be used when the prior on the number of components is Negative Binomial with parameter $r>0$ and $0<p<1$, with mean $mu =1+ r*p/(1-p)$. See (Argiento and Iorio 2019) for more details.

Usage

AM_prior_K_NegBin(n, gamma, r, p)

Arguments

Details

There are no default values.

Value

an AM_prior object, that is a vector of length n, reporting the values V(n,k) for k=1,...,n.

Examples

n <- 50
gamma <- 1
r <- 0.1
p <- 0.91
gam_nb <- 0.2381641
prior_K_nb <-  AM_prior_K_NegBin(n,gam_nb,r,p)
plot(prior_K_nb)

Description

This function computes the prior on the number of clusters, i.e. occupied components of the mixture for a Finite Dirichlet process when the prior on the component-weights of the mixture is a Dirichlet with parameter gamma (i.e. when unnormalized weights are distributed as Gamma($\gamma$,1)). This function can be used when the prior on the number of components is Shifted Poisson of parameter Lambda. See (Argiento and Iorio 2019) for more details.

Usage

AM_prior_K_Pois(n, gamma, Lambda)

Arguments

Details

There are no default values.

Value

an AM_prior object, that is a vector of length n, reporting the values of the prior on the number of clusters induced by the prior on M and w, i.e. p^*_k for k=1,...,n. See (Argiento and Iorio 2019) for more details.

Examples

n <- 82
Lambda <- 10
gam_po <- 0.1550195
prior_K_po <-  AM_prior_K_Pois(n,gam_po,Lambda)
plot(prior_K_po)

Description

Heuristic partitioning to minimise the expected loss function with respect to a given expected adjacency matrix. This function is built upon R-package salso's implementation of the salso function. See salso (Dahl et al. 2021) for more details.

Usage

AM_salso(
  eam,
  loss,
  maxNClusters = 0,
  nRuns = 16,
  maxZealousAttempts = 10,
  probSequentialAllocation = 0.5,
  nCores = 0
)

Arguments

Value

A numeric vector describing the estimated partition. The integer values represent the cluster labels of each item respectively.

Source

David B. Dahl and Devin J. Johnson and Peter Müller (2021). salso: Search Algorithms and Loss Functions for Bayesian Clustering. R package version 0.2.15.

Description

AntMAN: Anthology of Mixture ANalysis tools AntMan is an R package fitting Finite Bayesian Mixture models with a random number of components. The MCMC algorithm behind AntMAN is based on point processes and offers a more computationally efficient alternative to the Reversible Jump. Different mixture kernels can be specified: univariate Gaussian, multivariate Gaussian, univariate Poisson, and multivariate Bernoulli (Latent Class Analysis). For the parameters characterising the mixture kernel, we specify conjugate priors, with possibly user specified hyper-parameters. We allow for different choices on the prior on the number of components: Shifted Poisson, Negative Binomial, and Point Masses (i.e. mixtures with fixed number of components).

Package Philosophy

The main function of the AntMAN package is AM_mcmc_fit. AntMAN performs a Gibbs sampling in order to fit, in a Bayesian framework, a mixture model of a predefined type mix_kernel_hyperparams given a sample y. Additionally AntMAN allows the user to specify a prior on the number of components mix_components_prior and on the weights mix_weight_prior of the mixture. MCMC parameters mcmc_parameters need to be given as argument for the Gibbs sampler (number of interations, burn-in, ...). Initial values for the number of clusters (init_K) or a specific clustering allocation (init_clustering) can also be user-specified. Otherwise, by default, we initialise each element of the sample y to a different cluster allocation. This choice can be computationally inefficient.

For example, in order to identify clusters over a population of patients given a set of medical assumptions:

mcmc = AM_mcmc_parameters(niter=20000) 
mix = AM_mix_hyperparams_multiber () 
fit = AM_mcmc_fit (mix, mcmc) 
summary (fit)

In this example AM_mix_hyperparams_multiber is one of the possible mixtures to use.

AntMAN currently support four different mixtures :

AM_mix_hyperparams_unipois(alpha0, beta0) 
AM_mix_hyperparams_uninorm(m0, k0, nu0, sig02) 
AM_mix_hyperparams_multiber(a0, b0) 
AM_mix_hyperparams_multinorm(mu0, ka0, nu0, Lam0)

Additionally, three types of kernels on the prior number of components are available:

AM_mix_components_prior_pois()
AM_mix_components_prior_negbin() 
AM_mix_components_prior_dirac()

For example, in the context of image segmentation, if we know that there are 10 colours present, a prior dirac can be used :

mcmc = AM_mcmc_parameters(niter=20000) 
mix = AM_mix_hyperparams_multinorm () 
prior_component = AM_mix_components_prior_dirac(10) #  10 colours present
fit = AM_mcmc_fit (mix, prior_component, mcmc) 
summary (fit)

Description

Picture of brain activities from a teenager consuming drugs.

Usage

brain

Format

A list that contains dim a (W:width,H:height) pair, and pic a data frame (W*H pixels image in RGB format).

Source

https://www.flickr.com/photos/nida-nih/29741916012

References

Crowley TJ, Dalwani MS, Mikulich-Gilbertson SK, Young SE, Sakai JT, Raymond KM, et al. (2015) Adolescents' Neural Processing of Risky Decisions: Effects of Sex and Behavioral Disinhibition. PLoS ONE 10(7): e0132322. doi:10.1371/journal.pone.0132322

Examples

data(brain)

Description

The carcinoma data from Agresti (2002, 542) consist of seven dichotomous variables representing the ratings by seven pathologists of 118 slides on the presence or absence of carcinoma. The purpose of studying this data is to model "interobserver agreement" by examining how subjects might be divided into groups depending upon the consistency of their diagnoses.

Usage

carcinoma

Format

A data frame with 118 rows and 7 variables (from A to G).

References

Agresti A (2002). Categorical Data Analysis. John Wiley & Sons, Hoboken.

Examples

data(carcinoma)

Description

This data set considers the physical information of velocities (10^3 km/second) for 82 galaxies reported by Roeder (1990). These are drawn from six well-separated conic sections of the Corona Borealis region.

Usage

galaxy

Format

A data frame with X rows and Y variables.

A numeric vector giving the speed of galaxies (1000*(km/second))

Source

Roeder, K. (1990). Density estimation with confidence sets exemplified by superclusters and voids in the galaxies, Journal of the American Statistical Association, 85: 617-624.

Examples

data(galaxy)

Description

Given an AM_mcmc_output object, this function plots some useful information about the MCMC results regarding $M$ and $K$. Besides the PMFs, some of the diagnostic plots of the MCMC chain are visualised.

Usage

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

Arguments

Value

NULL. Called for side effects.

Description

plot the prior on the number of clusters for a given AM_prior object.

Usage

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

Arguments

Value

NULL. Called for side effects.

Description

Usage frequency of the word "said" in the Brown corpus

Usage

said

Format

A list with 500 observations on the frequency of said in different texts.

Source

https://www.kaggle.com/nltkdata/brown-corpus

References

Francis, W., and Kucera, H. (1982) Frequency Analysis of English Usage, Houghton Mifflin Company, Boston.

Examples

data(said)

Description

Given an AM_mcmc_configuration object, this function prints the summary information of the specified mcmc configuration.

Usage

## S3 method for class 'AM_mcmc_configuration'
summary(object, ...)

Arguments

Value

NULL. Called for side effects.

See Also

AM_mcmc_parameters

Description

Given an AM_mcmc_output object, this function prints the summary information pertaining to the given model output.

Usage

## S3 method for class 'AM_mcmc_output'
summary(object, ...)

Arguments

Value

NULL. Called for side effects.

See Also

AM_mcmc_fit, AM_mcmc_refit

Description

Given an AM_mix_components_prior object, this function prints the summary information of the specified prior on the number of components.

Usage

## S3 method for class 'AM_mix_components_prior'
summary(object, ...)

Arguments

Value

NULL. Called for side effects.

See Also

AM_mix_components_prior

Description

Given an AM_mix_hyperparams object, this function prints the summary information of the specified mixture hyperparameters.

Usage

## S3 method for class 'AM_mix_hyperparams'
summary(object, ...)

Arguments

Value

NULL. Called for side effects.

See Also

AM_mix_hyperparams

Description

Given an AM_mix_weights_prior object, this function prints the summary information of the specified mixture weights prior.

Usage

## S3 method for class 'AM_mix_weights_prior'
summary(object, ...)

Arguments

Value

NULL. Called for side effects.

See Also

AM_mix_weights_prior

Description

Given an AM_prior object, this function prints the summary information of the specified prior on the number of clusters.

Usage

## S3 method for class 'AM_prior'
summary(object, ...)

Arguments

Value

NULL. Called for side effects.

See Also

AM_prior