CensMixReg (version 3.1)

fmr.smn.cr: Censored mixture regression models based in the Scale Mixture of Normal (SMN) distribution

Description

Performs a Finite Mixture Regression (FMR) with censored based in the SMN using EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters.

Usage

fmr.smn.cr(cc, y, x, Abetas = NULL, sigma2 = NULL, pii = NULL, nu=NULL, g = NULL,
               family = "Normal", error = 0.00001, iter.max = 100)

Arguments

cc

Vector of censoring indicators. For each observation: 0 if non-censored, 1 if censored.

y

Vector of responses in case of right censoring.

x

Matrix or vector of covariates for each component

Abetas

Parameters of vector regression dimension \((p_j + 1)\) include or not intercept, j=1,...,G

sigma2

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 adjusted based on the size of these vectors)

pii

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 adjusted based on the size of these vectors)

nu

Initial value for the EM algorithm, nu it's degrees of freedom. Value of one size 1 (If Student's t or Slash) or size 2 (if Contaminated Normal)

g

Numbers of components

family

"T": fits a t-student regression mixture for censured data or "Normal": fits a Normal regression mixture censored data or "Slash": fits a Slash regression mixture censored data or "NormalC": fits a Contaminated Normal regression mixture censored data

error

define the stopping criterion of the algorithm

iter.max

the maximum number of iterations of the EM algorithm

References

Zeller, C. B., Cabral, C. R. B. and Lachos, V. H. (2016). Robust mixture regression modeling based on scale mixtures of skew-normal distributions. Test, 25, 375-396.

See Also

fmr.smn.cr,wage.rates

Examples

# NOT RUN {
  #See examples for the fmr.smn.cr function linked above.
# }