crch.stabsel  R Documentation 
Auxilirary function which allows to do stability selection on heteroscedastic
crch
models based on crch.boost
.
crch.stabsel(formula, data, ..., nu = 0.1, q, B = 100, thr = 0.9, maxit = 2000, data_percentage = 0.5)
formula 
a formula expression of the form 
data 
an optional data frame containing the variables occurring in the formulas. 
... 
Additional attributes to control the 
nu 
Boosting step size (see 
q 
Positive 
B 

thr 

maxit 
Positive 
data_percentage 
Percentage of data which should be sampled in each of the
iterations. Default (and suggested) is 
crch.boost
allows to perform gradient boosting on heteroscedastic
additive models. crch.stabsel
is a wrapper around the core crch.boost
algorithm to perform stability selection (see references).
Half of the data set (data
) is sampled B
times to perform boosting
(based on crch.boost
). Rather than perform the boosting iterations
until a certain stopping criterion is reached (e.g., maximum number of iterations
maxit
) the algorithm stops as soon as q
parameters have been selected.
The number of parameters is computed across both parameters location and scale.
Intercepts are not counted.
Returns an object of class "stabsel.crch"
containing the stability
selection summary and the new formula based on the stability selection.
table 
A table object containing the parameters which have been selected and the corresponding frequency of selection. 
formula.org 
Original formula used to perform the stability selection. 
formula.new 
New formula based including the coefficients selected during stability selection. 
family 
A list object which contains the distributionspecification from
the 
parameter 
List with the parameters used to perform the stability selection
including 
Meinhausen N, Buehlmann P (2010). Stability selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(4), 417–473. doi: 10.1111/j.14679868.2010.00740.x.
crch
, crch.boost
# generate data suppressWarnings(RNGversion("3.5.0")) set.seed(5) x < matrix(rnorm(1000*20),1000,20) y < rnorm(1000, 1 + x[,1]  1.5 * x[,2], exp(1 + 0.3*x[,3])) y < pmax(0, y) data < data.frame(cbind(y, x)) # fit model with maximum likelihood CRCH1 < crch(y ~ .., data = data, dist = "gaussian", left = 0) # Perform stability selection stabsel < crch.stabsel(y ~ .., data = data, dist = "gaussian", left = 0, q = 8, B = 5) # Show stability selection summary print(stabsel); plot(stabsel) CRCH2 < crch(stabsel$formula.new, data = data, dist = "gaussian", left = 0 ) BOOST < crch(stabsel$formula.new, data = data, dist = "gaussian", left = 0, control = crch.boost() ) ### AIC comparison sapply( list(CRCH1,CRCH2,BOOST), logLik )
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