LidarLDA_foldin: Title

In drvalle1/LidarLDA: Fits a Latent Dirichlet Allocation (LDA) model to Lidar data

Title

Description

Title

Usage

LidarLDA_foldin(
  y,
  n,
  nclust,
  gamma,
  ngibbs,
  nburn,
  phi.post,
  phi.estim,
  theta.post
)

Arguments

y	P x H matrix containing the number of returns for each pixel in each height class
n	P x H matrix containing the number of incoming light pulses for each pixel in each height class
nclust	maximum number of clusters (K)
gamma	parameter between 0 and 1 for the prior of the truncated stick-breaking prior
ngibbs	number of iterations for the MCMC algorithm
nburn	number of iterations to discard as burn in
phi.post	logical value (T or F) indicating if samples from the posterior distribution for phi are being provided or not
phi.estim	if phi.post is true, then the algorithm expects that this will be a large matrix containing samples from the posterior distribution for phi. Each row of this matrix should contain a separate sample. If phi.post is false, then this function expects that this is a K x H matrix containing an estimate (e.g., the posterior mean) of phi.
theta.post	should samples from the posterior distribution for theta be returned (TRUE or FALSE)? If FALSE, just the posterior mean is returned

Value

This function returns a list containing several elements:

• llk: log-likelihood for each iteration. This is a vector of size ngibbs.

• theta: estimated relative abundance of each cluster in each pixel. This consists of a matrix where rows are samples from the posterior distribution.

drvalle1/LidarLDA documentation built on July 27, 2024, 7:24 a.m.