| calibration.predx | R Documentation | 
Enables post-hoc quantile GAM calibration of the regression predictions
and models using the qgam tool.
## S3 method for class 'predx'
calibration(
  x,
  bs = "cr",
  k = 20,
  qu = 0.5,
  form = NULL,
  lsig = NULL,
  err = NULL,
  control = list(link = "identity"),
  argGam = NULL,
  ...
)
## S3 method for class 'caretx'
calibration(
  x,
  newdata = NULL,
  bs = "cr",
  k = 20,
  qu = 0.5,
  form = NULL,
  lsig = NULL,
  err = NULL,
  control = list(link = "identity"),
  argGam = NULL,
  ...
)
x | 
 a   | 
bs | 
 basis function for the smoother, ignored if a formula provided.  | 
k | 
 degrees of freedom for the smoother, ignored if a formula provided.  | 
qu | 
 quantile or quantiles for the calibration,
see:   | 
form | 
 optional GAM formula as specified by
  | 
lsig | 
 the value of the log learning rate used to create the
Gibbs posterior, see:   | 
err | 
 an upper bound on the error of the estimated quantile curve,
see:   | 
control | 
 a list of control parameters passed to
  | 
argGam | 
 a list of parameters to be passed to
  | 
... | 
 extra arguments passed to   | 
newdata | 
 a data frame with the test data.#'  | 
Currently, only quantile calibration for regression predx and caretx
objects is implemented. If a vector of quantiles is provided, the optimal
one is choosen based on the minimum RMSE
(more formal criteria are in development).
A list with the predx object
(.raw stores the uncalibrated predctions, .fitted stores the calibrated
predictions) along with the gamObject named cal_fit,
the chosen quantile value (qu) and values of explained deviance (qu_tbl).
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