dmbc_init {dmbc} | R Documentation |
dmbc_init()
is the main function that estimates a DMBC model.
dmbc_init(D, p, G, family, random.start, method, partition)
D |
A list whose elements are the dissimilarity matrices corresponding
to the judgments expressed by the S subjects/raters. These matrices
must be defined as a |
p |
A length-one numeric vector indicating the number of dimensions of the latent space. |
G |
A length-one numeric vector indicating the number of cluster to partition the S subjects. |
family |
A length-one character vector representing the type of data to analyze. Currently, it accepts only the 'binomial' value, but future developments will include the possibility to analyze continuous, multinomial and count data. |
random.start |
A length-one logical vector. If |
method |
A length-one character vector specifying the distance
measure to use in determining the initial partition. Allowed values are
those from the |
partition |
A length-one numeric vector providing the user-defined starting partition. |
A named list
with the following items:
z
: array of latent coordinates starting values
x
: numeric vector of initial cluster memberships
ng
: numeric vector of initial cluster sizes
alpha
: numeric vector of alpha starting values
eta
: numeric vector of eta starting values
sigma2
: numeric vector of sigma2 starting values
lambda
: numeric vector of lambda starting values
Sergio Venturini sergio.venturini@unicatt.it
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R
", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
dmbc()
for fitting a DMBC model.
data(simdiss, package = "dmbc")
dmbc_init(simdiss@diss, p = 2, G = 3, family = "binomial", random.start = TRUE)