iso_apply_calibration(dt, predict, calibration = "",
predict_range = NULL, calculate_error = FALSE,
quiet = default(quiet))

## Arguments

dt |
nested data table with `all_data` and calibration columns (see iso_generate_calibration) |

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

calibration |
name of the calibration to apply, must match the name used in iso_generate_calibration (if any) |

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. |

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. |

quiet |
whether to display (quiet=FALSE) or silence (quiet = TRUE) information messages. |

## Value

the data table with the following columns added to the nested `all_data`

\:

`predict`

column with suffix `_pred`

: the predicted value from applying the calibration

`predict`

column with suffix `_pred_se`

: the error of the predicated value propagated from the calibration. Only created if `calculate_error = TRUE`

.

`predict`

column with suffix `_pred_in_range`

: reports whether a data entry is within the range of the calibration by checking whether ALL dependent and independent variables in the regression model are within the range of the calibration - is set to FALSE if any(!) of them are not - i.e. this column provides information on whether new values are extrapolated beyond a calibration model and treat the extrapolated ones with the appropriate care. Note that all missing predicted values (due to missing parameters) are also automatically flagged as not in range