evaluateModel: Evaluate a model using the Akaike information criterion (AIC)
In genomaths/MethylIT.utils: MethylIT utility
.evaluateModel R Documentation
Evaluate a model using the Akaike information criterion (AIC)
Description
Evaluate a glm object using the Akaike information criterion
(AIC)
Usage
.evaluateModel(model, test = c("Wald", "LRT"))
Arguments
model
glm object
test
A character string matching one of 'Wald' or 'LRT'. If test =
'Wald', then the p-value of the Wald test for the coefficient of the
independent variable (treatment group) will be reported p-value. If
test = 'LRT', then the p-value from a likelihood ratio test given by
anova function from stats packages will be the
reported p-value for the group comparison.
Value
AIC value
genomaths/MethylIT.utils documentation built on July 4, 2023, 12:05 a.m.
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| .evaluateModel | R Documentation |
Evaluate a model using the Akaike information criterion (AIC)
Description
Evaluate a glm object using the Akaike information criterion (AIC)
Usage
.evaluateModel(model, test = c("Wald", "LRT"))
Arguments
model |
glm object |
test |
A character string matching one of 'Wald' or 'LRT'. If test =
'Wald', then the p-value of the Wald test for the coefficient of the
independent variable (treatment group) will be reported p-value. If
test = 'LRT', then the p-value from a likelihood ratio test given by
|
Value
AIC value
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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