Description Usage Arguments Author(s) References See Also Examples
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.
1 2 3 |
cc |
Vector of censoring indicators. For each observation: 0 if non-censored, 1 if censored. |
y |
Vector of responses in case of right censoring. |
nu |
Initial value for the EM algorithm, nu it's degrees of freedom. Value of one size 1 (If Student's t) |
mu |
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) |
Sigma |
a list of |
pii |
Initial value for the EM algorithm. The vector of initial values (dimension g) for the weights for each cluster. Must sum one! |
g |
Numbers of components |
get.init |
TRUE or FALSE. It indicates if the program (TRUE) is get the initial values or if the user (FALSE) entered manually the initial values. |
criteria |
It indicates if are calculated the criterion selection methods (AIC, BIC, EDC and ICL) |
group |
TRUE or FALSE. |
family |
"t": fits a t-student regression mixture for censured data or "Normal": fits a Normal regression mixture censored data |
error |
define the stopping criterion of the algorithm |
iter.max |
the maximum number of iterations of the EM algorithm |
uni.Sigma |
TRUE: if the covariance matrix are equals or FALSE if are not equal |
obs.prob |
TRUE or FALSE. |
kmeans.param |
Parameters for the k-means clustering algorithm |
Luis Benites Sanchez lbenitesanchez@gmail.com, Victor Hugo Lachos hlachos@ime.unicamp.br, Edgar J. Lopez Moreno edgar.javier.lopez.moreno@gmail.com
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.
1 | #See examples for the CensMmix function linked above.
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