apply a regression for calibration purposes — apply

this function can predict dependent variables (y) from calibration regressions as well as independent variables (xs) by regression inversion for single and multi-variate linear regressions

apply_regression(
  dt,
  predict,
  nested_model = FALSE,
  calculate_error = FALSE,
  model_data = model_data,
  model_name = model_name,
  model_fit = model_fit,
  model_params = model_params,
  predict_value = pred,
  predict_error = pred_se,
  predict_range = NULL
)

Arguments

dt	data table with calibrations
predict	which value to calculate, must be the regression's independent variable (regression is applied directly) or one of the independent variables (regression will be automatically inverted).
nested_model	whether the model is nested, if TRUE, must also provide `model_params`
calculate_error	whether to estimate the standard error from the calibration. Stores the result in the new `predict_error` column. If the `predict` variable is a dependent variable, will do so using the Wald method (as described in invest). Note that error calculation for dependent variables slows this function down a fair bit and is therefore disabled by default.
model_data	the nested model data column
model_name	new column with the model formulae or names if supplied
model_fit	the new model objects column
model_params	the nested model information (only relevant if `nest_model = TRUE`)
predict_value	the new column in the model_data that holds the predicted value
predict_error	the new column in the model_data that holds the error of the predicted value (always created but `NA` if `calculate_error = FALSE`)
predict_range	vector of 2 numbers. Only relevant for predicting dependent variables (regression inversion). If provided will be used for finding the solution for the predict variable. By default uses the range observed in the calibration variables. Specifying the `predict_range` is usually only necessary if the calibration range must be extrapolated significantely.