Eliminate a variable and fit a nonparametric additive model

Eliminates a specified variable and fits a nonparametric additive model with remaining variables, and returns validation set MSE. This is an internal function of the package, and designed to be called from model_backward.

Usage

eliminate(
  ind,
  train,
  val,
  yvar,
  family = gaussian(),
  s.vars = NULL,
  s.basedim = NULL,
  linear.vars = NULL,
  exclude.trunc = NULL,
  recursive = FALSE,
  recursive_colRange = NULL
)

Arguments

ind: An integer corresponding to the position of the predictor variable to be eliminated when fitting the model. (i.e. the function will combine s.vars and linear.vars in a single vector and eliminate the element corresponding to ind.)
train: The data set on which the model(s) will be trained. Must be a data set of class tsibble.
val: Validation data set. (The data set on which the model selection will be performed.) Must be a data set of class tsibble.
yvar: Name of the response variable as a character string.
family: A description of the error distribution and link function to be used in the model (see glm and family).
s.vars: A character vector of names of the predictor variables for which splines should be fitted (i.e. non-linear predictors).
s.basedim: Dimension of the bases used to represent the smooth terms corresponding to s.vars. (For more information refer mgcv::s().)
linear.vars: A character vector of names of the predictor variables that should be included linearly into the model (i.e. linear predictors).
exclude.trunc: The names of the predictor variables that should not be truncated for stable predictions as a character string. (Since the nonlinear functions are estimated using splines, extrapolation is not desirable. Hence, if any predictor variable in val that is treated non-linearly in the estimated model, will be truncated to be in the in-sample range before obtaining predictions. If any variables are listed here will be excluded from such truncation.)
recursive: Whether to obtain recursive forecasts or not (default - FALSE).
recursive_colRange: If recursive = TRUE, the range of column numbers in val.data to be filled with forecasts. Recursive/autoregressive forecasting is required when the lags of the response variable itself are used as predictor variables into the model. Make sure such lagged variables are positioned together in increasing lag order (i.e. lag_1, lag_2, ..., lag_m, lag_m = maximum lag used) in val.data, with no break in the lagged variable sequence even if some of the intermediate lags are not used as predictors.

Value

A numeric.