a previously boosted but not converged “crch.boost” object to continue.
dot
character specifying how to process formula parts with a dot (.) on the right-hand side. This can either be “separate” so that each formula part is expanded separately or “sequential” so that the parts are expanded sequentially conditional on all prior parts. Default is “separate”
mstop
method to find optimum stopping iteration. Default is “max” which is maxit. Alternatives are “aic” and “bic” for AIC and BIC optimization and “cv” for cross validation. mstop can also be a positive integer to set the number of boosting iterations. Then maxit is set to mstop and mstop=“max”.
nfolds
if mstopopt = “cv”, number of folds in cross validation.
foldid
if mstopopt = “cv”, an optional vector of values between 1 and nfold identifying the fold each observation is in. If supplied, nfolds can be missing.
maxvar
Positive numeric. Maximum number of parameters to be selected during each iteration (not including intercepts). Used for stability selection.
x, z, y, left, right, truncated, dist, df, link.scale, type, weights, offset, control
see crch.fit for details.
Details
crch.boost extends crch to fit censored (tobit) or truncated regression models with conditional heteroscedasticy by boosting. If crch.boost() is supplied as control in crch then crch.boost.fit is used as lower level fitting function. Note that crch.control() with method=boosting is equivalent to crch.boost(). Thus, boosting can more conveniently be called with crch(…, method = “boosting”).
Value
For crch.boost: A list with components named as the arguments. For crch.boost.fit: An object of class “crch.boost”, i.e., a list with the following elements.
coefficients
list of coefficients for location and scale. Scale coefficients are in log-scale. Coefficients are of optimum stopping stopping iteration specified by mstop.
df
if dist = “student”: degrees of freedom of student-t distribution. else NULL.
residuals
the residuals, that is response minus fitted values.
fitted.values
list of fitted location and scale parameters at optimum stopping iteration specified by mstop.
dist
assumed distribution for the dependent variable y.
cens
list of censoring points.
control
list of control parameters.
weights
case weights used for fitting.
offset
list of offsets for location and scale.
n
number of observations.
nobs
number of observations with non-zero weights.
loglik
log-likelihood.
link
a list with element “scale” containing the link objects for the scale model.
truncated
logical indicating wheter a truncated model has been fitted.
iterations
number of boosting iterations.
stepsize
boosting stepsize nu.
mstop
criterion used to find optimum stopping iteration.
mstopopt
optimum stopping iterations for different criteria.
standardize
list of center and scale values used to standardize response and regressors.
References
Messner JW, Mayr GJ, Zeileis A (2017). Non-Homogeneous Boosting for Predictor Selection in Ensemble Post-Processing. Monthly Weather Review, 145(1), 137–147. doi:10.1175/MWR-D-16-0088.1
See Also
crch, crch.control
Examples
library("crch")# generate datasuppressWarnings(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 likelihoodCRCH<-crch(y~.|., data =data, dist ="gaussian", left =0)# fit model with boostingboost<-crch(y~.|., data =data, dist ="gaussian", left =0, control =crch.boost(mstop ="aic"))# more conveniently, the same model can also be fit through# boost <- crch(y ~ .|., data = data, dist = "gaussian", left = 0,# method = "boosting", mstop = "aic")# AIC comparisonAIC(CRCH, boost)
