BICAICcapushe: AICcapushe and BICcapushe

In capushe: CAlibrating Penalities Using Slope HEuristics

AICcapushe and BICcapushe

Description

These functions return the model selected by the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC).

Usage

AICcapushe(data, n)
BICcapushe(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 or BIC.
AIC	The corresponding value of AIC (for AICcapushe only).
BIC	The corresponding value of BIC (for BICcapushe only).

Author(s)

Vincent Brault

References

http://www.math.univ-toulouse.fr/~maugis/CAPUSHE.html

http://www.math.u-psud.fr/~brault/capushe.html

Article: Baudry, J.-P., Maugis, C. and Michel, B. (2011) Slope heuristics: overview and implementation. Statistics and Computing, to appear. doi: 10.1007/ s11222-011-9236-1

See Also

capushe for a model selection function including AIC, BIC, the DDSE algorithm and the Djump algorithm.

Examples

data(datacapushe)
AICcapushe(datacapushe,n=1000)
BICcapushe(datacapushe,n=1000)

capushe documentation built on Nov. 27, 2023, 5:11 p.m.