lda_binomialVB: LDA with binomial entry and Stick-Breaking prior and...

In Rlda: Bayesian LDA for Mixed-Membership Clustering Analysis

Description

This method implements the Latent Dirichlet Allocation with Stick-Breaking prior for binomial data with Variational Inference. rlda.binomialVB works with frequency data.frame.

Usage

  rlda.binomialVB(data, loc.id, n_community, alpha0, alpha1,
  gamma, maxit=1000, thresh=0.0001)

Arguments

data	A abundance data.frame where each row is a sampling unit (i.e. Plots, Locations, Time, etc.) and each column is a categorical type of element (i.e. Species, Firms, Issues, etc.).
loc.id	ID variable in data.frame.
n_community	Total number of communities to return. It must be less than the total number of columns inside the data.
alpha0	Hyperparameter associated with the Beta prior Beta(alpha0, alpha1).
alpha1	Hyperparameter associated with the Beta prior Beta(alpha0, alpha1).
gamma	Hyperparameter associated with the Stick-Breaking prior.
maxit	Maximum number of iterations.
thresh	Threshold for ELBO convergence.

Details

rlda.binomialVB uses a modified Latent Dirichlet Allocation method to construct Mixed-Membership Clusters using Bayesian Variational Inference. The data must be a non-empty data.frame with the frequencies for each variable (column) in each observation (row).

Value

A R List with three elements:

Theta	The individual probability for each observation (ex: location) belong in each cluster (ex: community). It is a matrix with dimension equal n_gibbs by nrow(data) * n_community
Phi	The individual probability for each variable (ex: Specie) belong in each cluster (ex: community). It is a matrix with dimension equal n_gibbs by ncol(data) * n_community
ELBO	The vector ofELBO compute for each iteration.

Note

The Theta and Phi matrix can be obtained for the i-th gibbs sampling using matrix(Theta[i,], nrow = nrow(data), ncol = n_community) and matrix(Phi[i,], nrow = n_community, ncol = ncol(data)), respectively.

Author(s)

• Pedro Albuquerque.
  [e-mail]
  http://pedrounb.blogspot.com/

• Denis Valle.
  [e-mail]
  http://denisvalle.weebly.com/

• Daijiang Li.
  [e-mail]
  http://daijiang.name

References

• Blei, David M., Andrew Y. Ng, and Michael I. Jordan.
  "Latent dirichlet allocation." Journal of machine Learning research 3.Jan (2003): 993-1022. http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf

• Valle, Denis, et al.
  "Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method." Ecology letters 17.12 (2014): 1591-1601.

See Also

rlda.multinomial, rlda.bernoulli

Examples

	## Not run: 
		library(Rlda)
		# Set seed
		set.seed(5874)
		# Hyperparameters for each prior distribution
		gamma  <- 0.01
		alpha0 <- 0.01
		alpha1 <- 0.01
		# Execute the LDA for the Binomial entry
		res<- rlda.binomialVB(data=tmp, loc.id='loc.id', n_community=10,
		alpha0=0.01, alpha1=0.99, gamma=0.1, maxit=100, thresh=0.0001)
	
## End(Not run)

Rlda documentation built on May 1, 2019, 7:26 p.m.