AIC.glmBayes: Calculate Akaike Information Criterion for glmBayes models
In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities
Description
Usage
Arguments
Value
References
Description
Calculate Akaike Information Criterion for glmBayes models
Usage
1
2
## S3 method for class 'glmBayes'
AIC(model, correction = F)
Arguments
model
a fitted model
correction
whether or not to calculate the small sample-bias corrected AIC (AICc).
Value
numeric value
References
Sugiura, N. (1978). Further analysis of the data by Akaike's information criterion and the finite corrections. Comm. Statist. A 7, 13-26.
abnormally-distributed/cvreg documentation built on May 3, 2020, 3:45 p.m.
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Description Usage Arguments Value References
Description
Calculate Akaike Information Criterion for glmBayes models
Usage
1 2 | ## S3 method for class 'glmBayes'
AIC(model, correction = F)
|
Arguments
model |
a fitted model |
correction |
whether or not to calculate the small sample-bias corrected AIC (AICc). |
Value
numeric value
References
Sugiura, N. (1978). Further analysis of the data by Akaike's information criterion and the finite corrections. Comm. Statist. A 7, 13-26.
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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