Predict Concentrations from Calibration Curve

Uses a fitted calibration curve to predict concentrations from responses.

Usage

measure_calibration_predict(
  object,
  newdata,
  interval = c("none", "confidence", "prediction"),
  level = 0.95,
  ...
)

Arguments

object

A measure_calibration object from measure_calibration_fit().

newdata

A data frame containing the response values to predict from. Must contain a column with the same name as the response variable in the calibration formula.

interval

Type of interval to calculate:

• "none" (default): Point estimates only

• "confidence": Confidence intervals

• "prediction": Prediction intervals

level

Confidence level for intervals (default 0.95).

...

Additional arguments (unused).

Value

A tibble with columns:

• .pred_conc: Predicted concentration

• .pred_lower: Lower bound (if intervals requested)

• .pred_upper: Upper bound (if intervals requested)

Details

For inverse prediction (response -> concentration), the function uses root-finding when the model is quadratic. For linear models, direct algebraic inversion is used.

Interval Calculation

Intervals are calculated using the delta method for the inverse prediction. For quadratic models, intervals are approximate.

See also

measure_calibration_fit() for fitting calibration curves.

Examples

# Fit calibration curve
cal_data <- data.frame(
  nominal_conc = c(0, 10, 25, 50, 100),
  response = c(0.5, 15.2, 35.8, 72.1, 148.3)
)
cal <- measure_calibration_fit(cal_data, response ~ nominal_conc)

# Predict concentrations from new responses
unknowns <- data.frame(response = c(45, 85, 120))
measure_calibration_predict(cal, unknowns)
#> # A tibble: 3 × 1
#>   .pred_conc
#>        <dbl>
#> 1       30.6
#> 2       57.7
#> 3       81.4

# With prediction intervals
measure_calibration_predict(cal, unknowns, interval = "prediction")
#> # A tibble: 3 × 3
#>   .pred_conc .pred_lower .pred_upper
#>        <dbl>       <dbl>       <dbl>
#> 1       30.6        27.7        33.6
#> 2       57.7        54.8        60.7
#> 3       81.4        78.5        84.4