AICcapushe: AICcapushe
In capushe: Capushe, Data-Driven Slope Estimation and Dimension Jump
AICcapushe R Documentation
AICcapushe
Description
These functions return the model selected by the Akaike Information Criterion (AIC).
Usage
AICcapushe(data,n)
Arguments
data
data is a matrix or a data.frame with four columns of the same length
and each line corresponds to a model:
The first column contains the model names.
The second column contains the penalty shape values.
The third column contains the model complexity values.
The fourth column contains the minimum contrast value for each model.
n
n is the sample size.
Details
The penalty shape value should be increasing with respect to the complexity value (column 3).
The complexity values have to be positive.
n is necessary to compute AIC and BIC criteria. n is the size of
sample used to compute the contrast values given in the data matrix.
Do not confuse n with the size of the model collection which is the number
of rows of the data matrix.
Value
model The model selected by AIC.
Examples
data(datacapushe)
AICcapushe(datacapushe,n=1000)
capushe documentation built on Sept. 10, 2025, 10:31 a.m.
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| AICcapushe | R Documentation |
AICcapushe
Description
These functions return the model selected by the Akaike Information Criterion (AIC).
Usage
AICcapushe(data,n)
Arguments
data |
|
n |
|
Details
The penalty shape value should be increasing with respect to the complexity value (column 3).
The complexity values have to be positive.
n is necessary to compute AIC and BIC criteria. n is the size of
sample used to compute the contrast values given in the data matrix.
Do not confuse n with the size of the model collection which is the number
of rows of the data matrix.
Value
model The model selected by AIC.
Examples
data(datacapushe)
AICcapushe(datacapushe,n=1000)
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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