Description Usage Arguments Value Author(s) References See Also Examples
Performs a Finite Mixture of Scale Mixture Skew Normal Regression Model using EM-type algorithm (ECME) for iteratively computing maximum likelihood estimates of the parameters.
1 2 3 4 |
y |
the response matrix (dimension nx1) |
x1 |
Matrix or vector of covariates. |
Abetas |
Parameters of vector regression dimension (p + 1) include intercept |
medj |
a list of |
sigma2 |
a list of |
shape |
a list of |
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) |
g |
the number of cluster to be considered in fitting |
get.init |
if TRUE, the initial values are generated via k-means |
criteria |
It indicates if are calculated the criterion selection methods (AIC, BIC, EDC and ICL) |
group |
if TRUE, the vector with the classification of the response is returned |
family |
distribution famility to be used in fitting (Skew.t", "Skew.cn", "Skew.slash", "Skew.normal") |
error |
define the stopping criterion of the algorithm |
iter.max |
the maximum number of iterations of the EM algorithm |
obs.prob |
if TRUE, the posterior probability of each observation belonging to one of the g groups is reported |
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") |
show.convergence |
graphics of convergence for the parameters |
cp |
Cut Point |
The function returns a list with 16 elements detailed as
iter |
Number of iterations. |
criteria |
Attained criteria value. |
convergence |
Convergence reached or not. |
mu |
Location parameter estimate. |
sigma2 |
Scale parameter estimate. |
lambda |
Shape parameter estimate. |
pii |
Weight parameter estimate. |
nu |
Estimated degrees of freedom parameter. |
SE |
Standard Error estimates, if the output shows |
table |
Table containing the inference for the estimated parameters. |
loglik |
Log-likelihood value. |
AIC |
Akaike information criterion. |
BIC |
Bayesian information criterion. |
EDC |
Efficient Determination Criterion. |
ICL |
Information Completed Likelihood. |
time |
Processing time. |
Luis Benites Sanchez lbenitesanchez@gmail.com and Rocio Paola Maehara rmaeharaa@gmail.com and Victor Hugo Lachos hlachos@ime.unicamp.br
Basso, R. . M., Lachos, V. H., Cabral, C. R., Ghosh, P., 2010. Robust mixture modeling based on scale mixtures of skew-normal distributions. Computational Statistics & Data Analysis doi:10.1016/j.csda.2009.09.031.
Lachos, V. H., Ghosh, P., Arellano-Valle, R. B., 2010. Likelihood based inference for skew - normal independent linear mixed models. Statistica Sinica 20, 303 - 322.
1 | #See examples for the FMsmsnReg function linked above.
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