gof: Measures of Goodness-of-Fit
In mcglm: Multivariate Covariance Generalized Linear Models
gof R Documentation
Measures of Goodness-of-Fit
Description
Extract the pseudo Gaussian log-likelihood (plogLik),
pseudo Akaike Information Criterion (pAIC), pseudo Kullback-Leibler
Information Criterion (pKLIC) and pseudo Bayesian Information Criterion (pBIC)
for objects of mcglm class.
Usage
gof(object)
Arguments
object
an object or a list of objects representing a model
of mcglm class.
Value
Returns a data frame containing goodness-of-fit measures.
Author(s)
Wagner Hugo Bonat, wbonat@ufpr.br
Source
Bonat, W. H. (2018). Multiple Response Variables Regression
Models in R: The mcglm Package. Journal of Statistical Software, 84(4):1–30.
Wang, M. (2014). Generalized Estimating Equations in Longitudinal Data
Analysis: A Review and Recent Developments. Advances in Statistics, 1(1)1–13.
See Also
plogLik, pAIC, pKLIC and pBIC.
mcglm documentation built on Sept. 16, 2022, 1:06 a.m.
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| gof | R Documentation |
Measures of Goodness-of-Fit
Description
Extract the pseudo Gaussian log-likelihood (plogLik),
pseudo Akaike Information Criterion (pAIC), pseudo Kullback-Leibler
Information Criterion (pKLIC) and pseudo Bayesian Information Criterion (pBIC)
for objects of mcglm class.
Usage
gof(object)
Arguments
object |
an object or a list of objects representing a model
of |
Value
Returns a data frame containing goodness-of-fit measures.
Author(s)
Wagner Hugo Bonat, wbonat@ufpr.br
Source
Bonat, W. H. (2018). Multiple Response Variables Regression Models in R: The mcglm Package. Journal of Statistical Software, 84(4):1–30.
Wang, M. (2014). Generalized Estimating Equations in Longitudinal Data Analysis: A Review and Recent Developments. Advances in Statistics, 1(1)1–13.
See Also
plogLik, pAIC, pKLIC and pBIC.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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