MQR_qreg_gam: DEPRECIATED: Multiple Quantile Regression Using Generalised...
In jbrowell/ProbCast: Flexible Probabilistic Forecasting

MQR_qreg_gam

R Documentation

DEPRECIATED: Multiple Quantile Regression Using Generalised Additive Models and Linear Quantile Regression

Description

OLD VERSION SUPERCEDED BY qreg_gam. This function fits multiple conditional linear quantile regression models to the residuals of a generalised additive model using mgcv and with facilities for cross-validation.

Usage

MQR_qreg_gam(
  data,
  formula,
  formula_qr = NULL,
  model_res2 = F,
  formula_res2 = formula,
  quantiles = c(0.25, 0.5, 0.75),
  CVfolds = NULL,
  ...,
  use_bam = T,
  w = rep(1, nrow(data)),
  Sort = T,
  SortLimits = NULL
)

Arguments

`data`	A `data.frame` containing target and explanatory variables. May optionally contain a collumn called "kfold" with numbered/labeled folds and "Test" for test data. If `data` contains a column called "gam_pred" then gam modelling will be skipped and this will be used in quantile regression.
`formula_qr`	Formula for linear quantile regression model for GAM residuals. Term `gam_pred` is the prediction from the above GAM may be included in this formula.
`model_res2`	If `TRUE` also model squared residuals of GAM using a GAM. Defaults to `FALSE`.
`formula_res2`	Formula for GAM to predict squared residuals.
`quantiles`	The quantiles to fit models for.
`...`	Additional agruments passter to `gam()` or (`bam()`).
`use_bam`	If `TRUE` (default) then GAM is fit using (`bam()`) in stead of `gam()`. `bam` is better suited to large datasets but not all `gam` model options are available with `bam`. See `bam()` documentation for further details.
`w`	Weights on the contribution of data to model fit. See `gam()`.
`Sort`	`boolean` Sort quantiles using `SortQuantiles()`?
`SortLimits`	`Limits` argument to be passed to `SortQuantiles()`. Constrains quantiles to upper and lower limits given by `list(U=upperlim,L=lowerlim)`.
`formala`	A `formula` object with the response on the left of an ~ operator, and the terms, separated by + operators, on the right passed to `gam()` or `bam()` from `mgcv`.
`gbm_params`	List of parameters to be passed to `fit.gbm()`.
`cv_folds`	Control for cross-validation if not supplied in `data`.

Details

The returned predictive quantiles and GAM predictions are those produced out-of-sample for each cross-validation fold (using models trained on the remaining folds but not "Test" data). Predictive quantiles corresponding to "Test" data are produced using models trained on all non-test data.