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

predict

which value to calculate, must be one of the regression's independent variables

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 (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.

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 (only created if calculate_error = TRUE)

predict_range

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.