smsn.search: Find the best number of cluster for a determined data set.

In mixsmsn: Fitting Finite Mixture of Scale Mixture of Skew-Normal Distributions

Description

Search for the best fitting for number of cluster from g.min to g.max for a selected family and criteria for both univariate and multivariate distributions.

Usage

smsn.search(y, nu,
            g.min = 1, g.max = 3,
            family = "Skew.normal", criteria = "bic",
            error = 0.0001, iter.max = 100, 
            calc.im = FALSE, uni.Gama = FALSE, kmeans.param = NULL, ...)

Arguments

y	the response vector(matrix)
nu	the parameter of the scale variable (vector or scalar) of the SMSN family (kurtosis parameter). It is necessary to all distributions. For the "Skew.cn" must be a vector of length 2 and values in (0,1)
g.min	the minimum number of cluster to be modeled
g.max	the maximum number of cluster to be modeled
family	distribution famility to be used in fitting ("t", "Skew.t", "Skew.nc", "Skew.slash", "Skew.normal", "Normal")
criteria	the selection criteria method to be used ("aic", "bic", "edc", "icl")
error	the covergence maximum error
iter.max	the maximum number of iterations of the EM algorithm
calc.im	if TRUE, the infomation matrix is calculated and the starndard erros are reported
uni.Gama	if TRUE, the Gamma parameters are restricted to be the same for all clusters (Only valid in the multivariate case, p>1)
kmeans.param	a list with alternative parameters for the kmeans function when generating initial values, list(iter.max = 10, n.start = 1, algorithm = "Hartigan-Wong")
...	other parameters for the hist function

Value

Estimated values of the location, scale, skewness and kurtosis parameter from the optimum number of clusters.

Author(s)

Marcos Prates [e-mail], Victor Lachos [e-mail] and Celso Cabral [e-mail]

See Also

smsn.mix and smsn.mmix

Examples

 ## see \code{\link{bmi}} and \code{\link{faithful}}

mixsmsn documentation built on Oct. 6, 2021, 5:10 p.m.