tagmEM: Function to perform generalised t-augmented Gaussian mixture...

In cnfoley/tagmEM: Generalised t-augmented Gaussian mixture modelling using expectation maximisation

Description

Function to perform generalised t-augmented Gaussian mixture modelling using expectation-maximisation

Usage

tagmEM(
  data,
  obs_names = NULL,
  max_iter = 5000,
  tol = 1e-05,
  cluster_sizes = NULL,
  sigk_thresh = 1e-05,
  junk_mixture = TRUE,
  df = 4,
  junk_mean = NULL,
  junk_sd = NULL,
  stop_bic_iter = 5,
  min_clust_search = 8,
  results_list = list("all", "best"),
  rand_sample = seq(0.05, 0.4, by = 0.05)
)

Arguments

data	numeric object of univariate observations
obs_names	character vector of length the number of observations
max_iter	numeric integer denoting the maximum number of iterations before stopping the EM-algorithm's search for a maxima in the log-likelihood.
tol	numeric scalar denoting the maximum absolute difference between two computations of the log-likelihood with which we accept that a maxima in the log-likelihood has been computed.
cluster_sizes	integer varying from 0 to number of data points N
sigk_thresh	lower bound of estimated cluster standard deviation. NOTE: avoids singular estimates of the 'sd'
junk_mixture	default is TRUE
df	default is 'df = 4'
junk_mean	numeric scalar denoting the mean of the generalised t-distribution. By default mean is set to zero.
junk_sd	numeric scalar denoting the scale parameter in the generalised t-distribution
stop_bic_iter	numeric integer I, for computational efficiency - particularly when analysing large numbers of variants - we can stop the EM-algorithm if the BIC is monotonic increasing over the previous I increases in the number of clusters K. By default evidence supporting at least 10 clusters in the data is computed and so, for example, if the BIC from models which assume 6 clusters; 7 clusters; ... or; 10 clusters is monotonic increasing - in the number of clusters K -then the EM-algorithm is stopped and the model whose K minimises the BIC is returned.
min_clust_search	numeric integer which denotes the minimum number of clusters searched for in the data - default computes evidence supporting up to K=10 clusters which might explain any clustered heterogeneity in the data.
results_list	character list allowing users to choose whether to return a table with the variants assigned to: "all" of the clusters; a single "best" cluster or; both. By default we return both, i.e. results_list = list("all", "best").
rand_sample	random probability of being assignment to junk cluster

Value

Returned are: estimates of the putative number of clusters in the sample, allocation probabilities and summaries of the association estimates for each observation;

cnfoley/tagmEM documentation built on Feb. 1, 2021, 12:23 a.m.