crch.boost {crch} | R Documentation |
Auxiliary functions to fit crch
models via boosting.
Description
Auxiliary functions to fit crch
models via boosting
Usage
crch.boost(maxit = 100, nu = 0.1, start = NULL, dot = "separate",
mstop = c("max", "aic", "bic", "cv"), nfolds = 10, foldid = NULL,
maxvar = NULL)
crch.boost.fit(x, z, y, left, right, truncated = FALSE, dist = "gaussian",
df = NULL, link.scale = "log", type = "ml", weights = NULL, offset = NULL,
control = crch.boost())
Arguments
maxit |
the maximum number of boosting iterations. |
nu |
boosting step size. Default is 0.1. |
start |
a previously boosted but not converged |
dot |
character specifying how to process formula parts with a dot
( |
mstop |
method to find optimum stopping iteration. Default is |
nfolds |
if |
foldid |
if |
maxvar |
Positive |
x , z , y , left , right , truncated , dist , df , link.scale , type , weights , offset , control |
see |
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 |
df |
if |
residuals |
the residuals, that is response minus fitted values. |
fitted.values |
list of fitted location and scale parameters at
optimum stopping iteration specified by |
dist |
assumed distribution for the dependent variable |
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 |
truncated |
logical indicating wheter a truncated model has been fitted. |
iterations |
number of boosting iterations. |
stepsize |
boosting stepsize |
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 (2016). Non-Homogeneous Boosting for Predictor Selection in Ensemble Post-Processing. Working Papers, Faculty of Economics and Statistics, University of Innsbruck, url:http://econpapers.repec.org/paper/innwpaper/2016-04.htm.
See Also
Examples
# 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
CRCH <- crch(y ~ .|., data = data, dist = "gaussian", left = 0)
# fit model with boosting
boost <- 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 comparison
AIC(CRCH, boost)
# summary
summary(boost)
# plot
plot(boost)