note: this is optimized for inverting regressions, but should also do prediction for regular regression at some point

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 which value to calculate, must be one of the regression's independent variables whether the model is nested, if TRUE, must also provide model_params whether to estimate the standard error from the calibration (using the Wald method), stores the result in the new predict_error column. Note that error calculation slows this function down a fair bit and is therefore disabled by default. the nested model data column new column with the model formulae or names if supplied the new model objects column the nested model information (only relevant if nest_model = TRUE) the new column in the model_data that holds the predicted value the new column in the model_data that holds the error of the predicted value (only created if calculate_error = TRUE) vector of 2 numbers, 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 should be extrapolated significantely.