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This method implements the Latent Dirichlet Allocation with
Stick-Breaking prior for bernoulli data.
rlda.bernoulliMH works with binary data.frame.
rlda.bernoulliMH(data, loc.id, n_community, alpha0, alpha1, gamma,
n_gibbs, nadapt, ll_prior = TRUE, display_progress = TRUE)A binary 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.). The elements inside this data.frame must be Zeros and Ones.
Vector column from data with the repeated locations for Presence and Absence data.
Total number of communities to return. It must be less than
the total number of columns inside the data data.frame.
Hyperparameter associated with the Beta prior Beta(alpha0, alpha1).
Hyperparameter associated with the Beta prior Beta(alpha0, alpha1).
Hyperparameter associated with the Stick-Breaking prior.
Total number of Gibbs Samples.
Total number of Adaptations.
boolean scalar indicating TRUE if the log-likelihood
must be computed using also the priors or FALSE otherwise.
boolean scalar TRUE if the
Progress Bar must be showed and FALSE otherwise.
A R List with three elements:
The individual probability for each observation
(ex: location) belong in each cluster (ex: community). It is a matrix
with dimension equal n_gibbs by length(unique(loc.id)) * n_community
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
The vector of Log-Likelihoods compute for each Gibbs Sample.
rlda.bernoulliMH uses a modified Latent Dirichlet Allocation method
to construct Mixed-Membership Clusters using Bayesian Inference.
The data must be a non-empty data.frame with the binaries values
Zero or Ones for each variable (column) in each observation (row).
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.
# NOT RUN {
# }
# NOT RUN {
library(Rlda)
# Presence
data(presence)
# Set seed
set.seed(9842)
# Hyperparameters for each prior distribution
gamma <- 0.01
alpha0 <- 0.01
alpha1 <- 0.01
# Execute the LDA for the Bernoulli entry
res <- rlda.bernoulliMH(data=presence,loc.id=seq(1,nrow(presence)),
n_community=5, alpha0=0.01, alpha1=0.99, gamma=0.1,
n_gibbs=1000, nadapt=1000, ll_prior = TRUE, display_progress = TRUE)
# }
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