summary.fmem


<b>summary.fmem</b> displays the summary of the Bayesian analysis of flexible measurement error models.
This function produces a table with the summary statistics (mean, median and standard deviation) of the posterior distribution
as well as the 95% credible interval for the interest parameters. Further, this function displays goodness-of-fit statistics
such as DIC and LMPL.

Set of tools to perform the statistical inference based on the Bayesian approach for regression models under the assumption that independent additive errors follow normal, Student-t, slash, contaminated normal, Laplace or symmetric hyperbolic distributions, i.e., additive errors follow a scale mixtures of normal distributions. The regression models considered in this package are: (i) Generalized elliptical semi-parametric models, where both location and dispersion parameters of the response variable distribution include non-parametric additive components described by using B-splines; and (ii) Flexible measurement error models under the presence of homoscedastic and heteroscedastic random errors, which admit explanatory variables with and without measurement additive errors as well as the presence of a non-parametric components approximated by using B-splines.

summary.fmem: Produces a complete summary of flexible measurement error model fit

Description

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