capushe {capushe}  R Documentation 
CAlibrating Penalities Using Slope HEuristics (CAPUSHE)
Description
The capushe
function proposes two algorithms based on the slope heuristics
to calibrate penalties in the context of model selection via penalization.
Usage
capushe(data,n=0,pct=0.15,point=0,psi.rlm=psi.bisquare,scoef=2,
Careajump=0,Ctresh=0)
Arguments
data 

n 

pct 
Minimum percentage of points for the plateau selection.
See 
point 
Minimum number of point for the plateau selection (See 
psi.rlm 
Weight function used by 
scoef 
Ratio parameter. Default value is 2. 
Careajump 
Constant of jump area (See 
Ctresh 
Maximal treshold for the complexity associated to the penalty coefficient (See 
Details
The model \hat{m}
selected by the procedure fulfills
\hat{m}=
argmin \gamma_n (\hat{s}_m)+scoef\times \kappa\times pen_{shape}(m)
where
\kappa
is the penalty coefficient.\gamma_n
is the empirical contrast.\hat{s}_m
is the estimator for the modelm
.scoef
is the ratio parameter.pen_{shape}
is the penalty shape.
The capushe function calls the functions DDSE
and
Djump
to calibrate \kappa
, see the description of these functions
for more details.
In the case of equality between two penalty shape values, only the model with the
smallest contrast is considered.
Value
@DDSE 
A list returned by the 
@DDSE@model 
The 
@DDSE@kappa 
The vector of the successive slope values. 
@DDSE@ModelHat 
A list providing details about the model selected by the 
@DDSE@interval 
A list about the "slope interval" corresponding to the
plateau selected in 
@DDSE@graph 
A list computed for the 
@Djump 
A list returned by the 
@Djump@model 
The 
@Djump@ModelHat 
A list providing details about the model selected by the 
@Djump@graph 
A list computed for the 
@AIC_capushe 
A list returned by the 
@BIC_capushe 
A list returned by the 
@n 
Sample size. 
Author(s)
Vincent Brault
References
http://www.math.univtoulouse.fr/~maugis/CAPUSHE.html
http://www.math.upsud.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/ s1122201192361
See Also
Djump
, DDSE
, AIC
or BIC
to use only one of these model selection functions.
plot
for graphical displays of DDSE
and Djump.
Examples
data(datacapushe)
capushe(datacapushe)
capushe(datacapushe,1000)