Performs a Finite Mixture Censored multivariate (FM-MC) Student-t and Normal distribution using using EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters.
CensMmix(cc, y, nu=3, mu=NULL, Sigma = NULL, pii = NULL, g = NULL, get.init = TRUE,
criteria = TRUE, group = FALSE, family = "Normal", error = 0.0001,
iter.max = 300, uni.Sigma = FALSE, obs.prob= FALSE, kmeans.param = NULL)Vector of censoring indicators. For each observation: 0 if non-censored, 1 if censored.
Vector of responses in case of right censoring.
Initial value for the EM algorithm, nu it's degrees of freedom. Value of one size 1 (If Student's t)
Initial value for the EM algorithm. Each of them must be a vector of length g.(the algorithm considers the number of components to be fitted based on the size of these vectors)
a list of g arguments of matrices of initial values (dimension pxp) for the scale parameters
Initial value for the EM algorithm. The vector of initial values (dimension g) for the weights for each cluster. Must sum one!
Numbers of components
TRUE or FALSE. It indicates if the program (TRUE) is get the initial values or if the user (FALSE) entered manually the initial values.
It indicates if are calculated the criterion selection methods (AIC, BIC, EDC and ICL)
TRUE or FALSE.
"t": fits a t-student regression mixture for censured data or "Normal": fits a Normal regression mixture censored data
define the stopping criterion of the algorithm
the maximum number of iterations of the EM algorithm
TRUE: if the covariance matrix are equals or FALSE if are not equal
TRUE or FALSE.
Parameters for the k-means clustering algorithm
Arellano-Valle, R. B., Castro, L., Gonzalez-Farias, G. & Munos Gajardo, K. (2012). Student-t censored regression model: properties and inference. Statistical Methods and Applications, 21(4), 453-473.
Dempster, A., Laird, N. & Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B,39, 1-38.
Peel, D. & McLachlan, G. J. (2000). Robust mixture modelling using the t distribution. Statistics and Computing,10(4), 339-348.
Karlsson, M. & Laitila, T. (2014). Finite mixture modeling of censored regression models. Statistical Papers,55(3), 627-642.
Basso,R.M.,Lachos,V.H.,Cabral,C.R.B. & Ghosh,P. (2010). Robust mixture modeling based on scale mixtures of skew-normal distributions. Computational Statistics & Data Analysis, 54(12), 2926-2941.
Basford, K., Greenway, D.,McLachlan,G. & Peel,D. (1997). Standard errors of fitted component means of normal mixtures. Computational Statistics,12, 1-18.
# NOT RUN {
#See examples for the CensMmix function linked above.
# }
Run the code above in your browser using DataLab