Description Usage Arguments Author(s) References See Also Examples
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.
1 2 |
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 |
Luis Benites Sanchez lbenitesanchez@gmail.com, Victor Hugo Lachos hlachos@ime.unicamp.br
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.
1 | #See examples for the fmr.smn.cr function linked above.
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