validation: validation

In capushe: CAlibrating Penalities Using Slope HEuristics

validation

Description

validation checks that the slope heuristics can be applied confidently.

Usage

validation(x,data2,...)

Arguments

x	x must be an object of class capushe or DDSE, in practice an output of the capushe function or the DDSE function.
data2	data2 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.
...	If newwindow==TRUE, a new window is created for the plot.

Details

The validation function plots the additional and more complex models data2 to check that the linear relation between the penalty shape values and the contrast values (which is recorded in x) is valid for the more complex models.

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 more general model selection function including AIC, BIC, the DDSE algorithm and the Djump algorithm.

Examples

data(datapartialcapushe)
capushepartial=capushe(datapartialcapushe)
data(datavalidcapushe)
validation(capushepartial,datavalidcapushe) ## The slope heuristics should not 
## be applied for datapartialcapushe.
data(datacapushe)
plot(capushe(datacapushe))

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