| smsn.search {mixsmsn} | R Documentation |
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.
Author(s)
Marcos Prates marcosop@est.ufmg.br, Victor Lachos hlachos@ime.unicamp.br and Celso Cabral celsoromulo@gmail.com
See Also
Examples
## see \code{\link{bmi}} and \code{\link{faithful}}