Fit a finite mixture model using TMB

Source: R/clustTMB.R

Fit a finite mixture model using TMB

clustTMB(
  response = NULL,
  expertformula = ~1,
  gatingformula = ~1,
  expertdata = NULL,
  gatingdata = NULL,
  family = gaussian(link = "identity"),
  Offset = NULL,
  G = 2,
  rr = list(spatial = NULL, temporal = NULL, random = NULL),
  covariance.structure = NULL,
  Start = list(),
  Map = list(),
  initialization.args = list(control = init.options()),
  spatial.list = list(loc = NULL, mesh = NULL, init.range = list(gating.range = NULL,
    expert.range = NULL)),
  projection.dat = NULL,
  control = run.options()
)

Arguments

response

A numeric vector, matrix, or data frame of observations. When data are multivariate, rows correspond to observations and columns correspond to the multivariate response.

expertformula

Formula defining expert model. This formula corresponds to the covariates included in the response densities. Defaults to intercept only (~1) when no covariates are used.

gatingformula

Formula defining gating model. This formula corresponds to the covariates included in the mixing proportions (logistic regression). Defaults to intercept only (~1) when no covariates are used. When a random effects term is included in the gating network, this formula will be updated so that the intercept term is removed.

expertdata

Data frame containing expert model covariates.

gatingdata

Data frame containing gating model covariates.

family

Statistical distribution and link function of observations.

Offset

Constant in expertformula only used to offset density expectation.

G

Integer specifying the number of clusters.

rr

List specifying dimension of rank reduction in spatial, temporal, and/or random effects. Dimension must be smaller than the total dimension of the response. Rank reduction is applied only to the expertformula random effects. The rank reduction reduces the dimensionality of a correlated multivariate response to a smaller dimension independent response. When used, the covariance structure of the response is switched to 'Diagonal.' Defaults to NULL, no rank reduction. If rank reduction is used in conjunction with a random effect, that random effect must also be specified in the expert formula. Currently, rank reduction on temporal random effects is disabled.

covariance.structure

A character string specifying the covariance structure of the response using mclust naming scheme. See description of modelNames under ?Mclust for details.

Start

Set initial values for random effects parameters (fixed and random terms)

Map

Vector indicating parameter maps, see ?TMB::MakeADFun() for details. Defaults in clustTMB control this map argument and user input is limited

initialization.args

A list consisting of initialization settings used to generate initial values. control Calls init.options() to generate settings for initial values. Arguments of init.options() can be specified by the user.

  1. init.method - Single character string indicating initial clustering method. Methods include: hc, quantile, random, mclust, kmeans, mixed, user. Defaults to 'hc'. In the case where data are univariate and there are no covariates in the gating/expert formula, this defaults to 'quantile'

  2. hc.options - Named list of two character strings specifying hc modelName and hcUse when init.method = 'hc'. The default modelName is 'VVV' and the default use is 'SVD' unless gating/expert covariates specified, in which case default in VARS. See ?mclust::mclust.options for complete list of options.

  3. mix.method - String stating initialization method for mixed-type data (init.method = 'mixed'). Current default when Tweedie family specified. Options include: Gower kmeans (default), Gower hclust, and kproto.

  4. user - Numeric or character vector defining user specified initial classification. init.method must be set to 'user' when using this option.

spatial.list

List of data objects needed when fitting a spatial GMRF model

projection.dat

Spatial Points class of projection coordinates or Spatial Points Dataframe containing projection coordinates and projection covariates

control

List controlling whether models are run and whether standard errors are calculated.

Value

list of objects from fitted model

Examples

data("faithful")
m1 <- clustTMB(response = faithful, covariance.structure = "VVV")
plot(faithful$eruptions, faithful$waiting, pch = 16, col = m1$report$classification + 1)