Generate a calibration for a specific variable based on one or multiple calibration models. Requires properly nested and grouped data, see iso_prepare_for_calibration for details. Note that to calibrate different variables, separate calls to this function should be issued each with different
iso_generate_calibration(dt, model, calibration = "", is_std_peak = default(is_std_peak), min_n_datapoints = 2, quiet = default(quiet), is_standard = NULL)
nested data table with column
a single regression model or a list of multiple alternative regression models for the calibration. If a named list is provided, the name(s) will be used instead of the formulaes for the model identification column. Note that if multiple models are provided, the entire data table rows will be duplicated to consider the different models in parallel.
an informative name for the calibration (could be e.g.
column or filter condition to determine which subset of data to actually use for the calibration (default is the
the minimum number of data points required for applying the model(s). Note that there is always an additional check to make sure the minimum number of degrees of freedom for each model is met. If the minimum number of degrees of freedom required is not met, the model will/can not be calculated no matter what
whether to display (quiet=FALSE) or silence (quiet = TRUE) information messages.
the data table with the following columns added (prefixed by the
calibration parameter if provided):
calib: the name of the calibration if provided in the
model parameter, otherwise the formula
calib_ok: a TRUE/FALSE column indicating whether there was enough data for calibration to be generated
calib_params: a nested dataframe that holds the actual regression model fit, coefficients, summary and data range. These parameters are most easily accessed using the functions
iso_unnest_calibration_range, or directly via
all_data: a new column within the nested
all_data that holds the residuals for all standards used in the regression model