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Predict from a Calibration Curve

Source: R/calibration_curve.R
predict.specproc_calibration.Rd

Computes the concentrations of new samples from their signal, by inverse prediction from a curve fitted with calibration_curve(), or the expected signal at given concentrations, with confidence or prediction intervals.

Usage

# S3 method for class 'specproc_calibration'
predict(
  object,
  newdata,
  replicates = 1,
  level = 0.95,
  type = "concentration",
  interval = "prediction",
  ...
)

Arguments

object

A curve fitted with calibration_curve().

newdata

For type = "concentration", the signals of the new samples: a numeric vector, or a data frame with the signal column used for the calibration. For type = "signal", their concentrations: a numeric vector or a data frame with the concentration column.

replicates

The number of replicate measurements averaged in each signal. Default is 1.

level

The confidence level of the intervals. Default is 0.95.

type

"concentration" (default, inverse prediction) or "signal".

interval

For type = "signal": "prediction" (default) or "confidence".

...

Not used.

Value

For type = "concentration", a tibble with the signal, the predicted concentration, its standard error se, the interval (lower, upper) and whether it is below the limit of detection (below_lod) or of quantification (below_loq). For type = "signal", a tibble with the concentration, the predicted signal, its standard error se and the interval (lower, upper).

Details

With type = "concentration" (default), the interval of each concentration combines the uncertainty of the curve with the noise of the new signal, the mean of replicates measurements (see calibration_curve()).

With type = "signal", newdata holds concentrations, and the interval is either the confidence interval of the mean signal (interval = "confidence") or the prediction interval of a new signal, the mean of replicates measurements (interval = "prediction", default). For a weighted curve, the noise of a new signal follows the weights at its concentration (numeric weights give new samples a weight of 1).

See also

calibration_curve(), plot_calibration()

Examples

data(forageLIBS)
# the K I 769.90 nm line, normalized to the C I 247.86 nm line of the matrix
lines <- line_intensities(forageLIBS[-(1:14)], c(C = 247.856, K = 769.896), baseline = TRUE)
standards <- data.frame(
  K = forageLIBS$K,
  signal = lines$intensity[lines$line == "K"] / lines$intensity[lines$line == "C"]
)
cal <- calibration_curve(standards[1:300, ], signal, K)
predict(cal, standards$signal[301:305])
#> # A tibble: 5 × 7
#>   signal concentration    se   lower upper below_lod below_loq
#>    <dbl>         <dbl> <dbl>   <dbl> <dbl> <lgl>     <lgl>    
#> 1  0.459          1.36  1.03 -0.665   3.38 TRUE      TRUE     
#> 2  0.543          1.97  1.03 -0.0538  3.99 TRUE      TRUE     
#> 3  0.580          2.23  1.03  0.212   4.25 TRUE      TRUE     
#> 4  0.492          1.59  1.03 -0.427   3.62 TRUE      TRUE     
#> 5  0.481          1.52  1.03 -0.507   3.54 TRUE      TRUE     
# the signal expected at given contents (% K)
predict(cal, c(1, 2, 3), type = "signal")
#> # A tibble: 3 × 5
#>   concentration signal    se lower upper
#>           <dbl>  <dbl> <dbl> <dbl> <dbl>
#> 1             1  0.409 0.143 0.128 0.690
#> 2             2  0.548 0.142 0.268 0.827
#> 3             3  0.686 0.143 0.405 0.967
predict(cal, c(1, 2, 3), type = "signal", interval = "confidence")
#> # A tibble: 3 × 5
#>   concentration signal      se lower upper
#>           <dbl>  <dbl>   <dbl> <dbl> <dbl>
#> 1             1  0.409 0.0178  0.374 0.444
#> 2             2  0.548 0.00819 0.531 0.564
#> 3             3  0.686 0.0174  0.652 0.720