crch.boost | R Documentation |

`crch`

models via boosting.Auxiliary functions to fit `crch`

models via boosting

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())

`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 |

`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")`

.

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. |

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.

`crch`

, `crch.control`

# 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)

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