initial_cluster: initial clustering of the data set

In hhsmm: Hidden Hybrid Markov/Semi-Markov Model Fitting

initial clustering of the data set

Description

Provides an initial clustering for a data of class "hhsmmdata" which determines the initial states and mixture components (if necessary) to be used for initial parameter and model estimation

Usage

initial_cluster(
  train,
  nstate,
  nmix,
  ltr = FALSE,
  equispace = FALSE,
  final.absorb = FALSE,
  verbose = FALSE,
  regress = FALSE,
  resp.ind = 1
)

Arguments

train	the train data set of class "hhsmmdata", which can also contain missing data (NA or NaN)
nstate	number of states
nmix	number of mixture components which is of one of the following forms: a vector of positive (non-zero) integers of length nstate a positive (non-zero) integer the text "auto": the number of mixture components will be determined automatically based on the within cluster sum of squares NULL if no mixture distribution is not considered as the emission. This option is usefull for the nonparametric emission distribution (nonpar_mstep and dnonpar)
ltr	logical. if TRUE a left to right hidden hybrid Markov/semi-Markov model is assumed
equispace	logical. if TRUE the left to right clustering will be performed simply with equal time spaces. This option is suitable for speech recognition applications
final.absorb	logical. if TRUE the final state of the sequence is assumed to be the absorbance state
verbose	logical. if TRUE the outputs will be printed
regress	logical. if TRUE the linear regression clustering will be performed
resp.ind	the column indices of the response variables for the linear regression clustering approach. The default is 1, which means that the first column is the univariate response variable

Details

In reliability applications, the hhsmm models are often left-to-right and the modeling aims to predict the future states. In such cases, the ltr=TRUE and final.absorb=TRUE should be set.

Value

a list containing the following items:

• clust.X a list of clustered observations for each sequence and state

• mix.clus a list of the clusters for the mixtures for each state

• state.clus the exact state clusters of each observation (available if ltr=FALSE)

• nmix the number of mixture components (a vector of positive (non-zero) integers of length nstate)

• ltr logical. if TRUE a left to right hidden hybrid Markov/semi-Markov model is assumed

• final.absorb logical. if TRUE the final state of the sequence is assumed to be the absorbance state

• miss logical. if TRUE the train$x matrix contains missing data (NA or NaN)

Author(s)

Morteza Amini, [e-mail], Afarin Bayat, [e-mail]

Examples

J <- 3
initial <- c(1, 0, 0)
semi <- c(FALSE, TRUE, FALSE)
P <- matrix(c(0.8, 0.1, 0.1, 0.5, 0, 0.5, 0.1, 0.2, 0.7), nrow = J, 
byrow = TRUE)
par <- list(mu = list(list(7, 8), list(10, 9, 11), list(12, 14)),
sigma = list(list(3.8, 4.9), list(4.3, 4.2, 5.4), list(4.5, 6.1)),
mix.p = list(c(0.3, 0.7), c(0.2, 0.3, 0.5), c(0.5, 0.5)))
sojourn <- list(shape = c(0, 3, 0), scale = c(0, 10, 0), type = "gamma")
model <- hhsmmspec(init = initial, transition = P, parms.emis = par,
dens.emis = dmixmvnorm, sojourn = sojourn, semi = semi)
train <- simulate(model, nsim = c(10, 8, 8, 18), seed = 1234, 
remission = rmixmvnorm)
clus = initial_cluster(train, nstate = 3, nmix = c(2 ,2, 2),ltr = FALSE,
final.absorb = FALSE, verbose = TRUE)

hhsmm documentation built on Sept. 11, 2024, 7:34 p.m.