mixsmsn (version 1.1-10)

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

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.

See Also

smsn.mix and smsn.mmix

Examples

# NOT RUN {
 ## see \code{\link{bmi}} and \code{\link{faithful}}
# }