IMIFA-package: IMIFA: Infinite Mixtures of Infinite Factor Analysers and...
In IMIFA: Infinite Mixtures of Infinite Factor Analysers and Related Models
IMIFA-package R Documentation
IMIFA: Infinite Mixtures of Infinite Factor Analysers and Related Models
Description
A package for Bayesian nonparametric clustering of high-dimensional data sets, providing functions for fitting, diagnostic tools and plotting for Infinite Mixtures of Infinite Factor Analysers and the full suite of related models introduced by Murphy et al. (2020) <doi: 10.1214/19-BA1179>. Allows model based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.
Details
Type: Package
Package: IMIFA
Version: 2.1.10
Date: 2022-12-19 (this version), 2017-02-02 (original release)
Licence: GPL (>=2)
Usage
The three most important functions in the IMIFA package are: mcmc_IMIFA, for fitting the model, get_IMIFA_results, for extracting results from objects of the "IMIFA" class generated by mcmc_IMIFA, and the dedicated plotting function plot.Results_IMIFA, for plotting results pertaining to parameters of inferential interest from objects of class "Results_IMIFA" generated by get_IMIFA_results.
Other functions also exist, e.g. for simulating data from a multivariate mixture of factor analysers, many functions for soliciting good priors, and many functions related to plotting.
mcmc_IMIFA:
This function estimates models in the IMIFA family under the Bayesian paradigm. Most importantly, one must specify the method in the form of an acronym (e.g. "MIFA" for Mixtures of Infinite Factor Analysers) and ranges of values for range.G, the number of clusters, and range.Q, the number(s) of (cluster-specific) latent factors as required by said method.
get_IMIFA_results:
Raw simulation objects generated by mcmc_IMIFA() are passed to this function in order to extract results of interest and conduct further post-processing if necessary.
plot.Results_IMIFA:
Results obtained from get_IMIFA_Results are passed to this function with the type of plot desired specified by plot.meth (e.g. "trace") and the parameter of interest specified by param (e.g. "loadings").
References
Murphy, K., Viroli, C., and Gormley, I. C. (2020) Infinite mixtures of infinite factor analysers, Bayesian Analysis, 15(3): 937-963. <doi:10.1214/19-BA1179>.
See Also
Further details and examples are given in the associated vignette document:
vignette("IMIFA", package = "IMIFA")
Author(s)
Keefe Murphy [aut, cre], Cinzia Viroli [ctb], Isobel Claire Gormley [ctb]
Maintainer: Keefe Murphy - <keefe.murphy@mu.ie>
See Also
Useful links:
IMIFA documentation built on Dec. 28, 2022, 1:58 a.m.
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| IMIFA-package | R Documentation |
IMIFA: Infinite Mixtures of Infinite Factor Analysers and Related Models
Description
A package for Bayesian nonparametric clustering of high-dimensional data sets, providing functions for fitting, diagnostic tools and plotting for Infinite Mixtures of Infinite Factor Analysers and the full suite of related models introduced by Murphy et al. (2020) <doi: 10.1214/19-BA1179>. Allows model based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.
Details
Type: Package
Package: IMIFA
Version: 2.1.10
Date: 2022-12-19 (this version), 2017-02-02 (original release)
Licence: GPL (>=2)
Usage
The three most important functions in the IMIFA package are: mcmc_IMIFA, for fitting the model, get_IMIFA_results, for extracting results from objects of the "IMIFA" class generated by mcmc_IMIFA, and the dedicated plotting function plot.Results_IMIFA, for plotting results pertaining to parameters of inferential interest from objects of class "Results_IMIFA" generated by get_IMIFA_results.
Other functions also exist, e.g. for simulating data from a multivariate mixture of factor analysers, many functions for soliciting good priors, and many functions related to plotting.
mcmc_IMIFA:
This function estimates models in the IMIFA family under the Bayesian paradigm. Most importantly, one must specify the method in the form of an acronym (e.g. "MIFA" for Mixtures of Infinite Factor Analysers) and ranges of values for range.G, the number of clusters, and range.Q, the number(s) of (cluster-specific) latent factors as required by said method.
get_IMIFA_results:
Raw simulation objects generated by mcmc_IMIFA() are passed to this function in order to extract results of interest and conduct further post-processing if necessary.
plot.Results_IMIFA:
Results obtained from get_IMIFA_Results are passed to this function with the type of plot desired specified by plot.meth (e.g. "trace") and the parameter of interest specified by param (e.g. "loadings").
References
Murphy, K., Viroli, C., and Gormley, I. C. (2020) Infinite mixtures of infinite factor analysers, Bayesian Analysis, 15(3): 937-963. <doi:10.1214/19-BA1179>.
See Also
Further details and examples are given in the associated vignette document:
vignette("IMIFA", package = "IMIFA")
Author(s)
Keefe Murphy [aut, cre], Cinzia Viroli [ctb], Isobel Claire Gormley [ctb]
Maintainer: Keefe Murphy - <keefe.murphy@mu.ie>
See Also
Useful links:
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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