EnvStats (version 2.1.0)

inversePredictCalibrate: Predict Concentration Using Calibration

Description

Predict concentration using a calibration line (or curve) and inverse regression.

Usage

inversePredictCalibrate(object, obs.y = NULL, 
    n.points = ifelse(is.null(obs.y), 100, length(obs.y)), 
    intervals = FALSE, coverage = 0.99, simultaneous = FALSE, 
    individual = FALSE, trace = FALSE)

Arguments

object

an object of class "calibrate" that is the result of calling the function calibrate.

obs.y

optional numeric vector of observed values for the machine signal. The default value is obs.y=NULL, in which case obs.y is set equal to a vector of values (of length n.points) ranging from the minimum to the

n.points

optional integer indicating the number of points at which to predict concentrations (i.e., perform inverse regression). The default value is n.points=100. This argument is ignored when obs.y is supplied.

intervals

optional logical scalar indicating whether to compute confidence intervals for the predicted concentrations. The default value is intervals=FALSE.

coverage

optional numeric scalar between 0 and 1 indicating the confidence level associated with the confidence intervals for the predicted concentrations. The default value is coverage=0.99.

simultaneous

optional logical scalar indicating whether to base the confidence intervals for the predicted values on simultaneous or non-simultaneous prediction limits. The default value is simultaneous=FALSE.

individual

optional logical scalar indicating whether to base the confidence intervals for the predicted values on prediction limits for the mean (individual=FALSE) or prediction limits for an individual observation (individual=TRUE

trace

optional logical scalar indicating whether to print out (trace) the progress of the inverse prediction for each of the specified values of obs.y. The default value is trace=FALSE.

Value

• A numeric matrix containing the results of the inverse calibration. The first two columns are labeled obs.y and pred.x containing the values of the argument obs.y and the predicted values of x (the concentration), respectively. If intervals=TRUE, then the matrix also contains the columns lpl.x and upl.x corresponding to the lower and upper prediction limits for x. Also, if intervals=TRUE, then the matrix has the attributes coverage (the value of the argument coverage) and simultaneous (the value of the argument simultaneous).

Details

A simple and frequently used calibration model is a straight line where the response variable $S$ denotes the signal of the machine and the predictor variable $C$ denotes the true concentration in the physical sample. The error term is assumed to follow a normal distribution with mean 0. Note that the average value of the signal for a blank ($C = 0$) is the intercept. Other possible calibration models include higher order polynomial models such as a quadratic or cubic model. In a typical setup, a small number of samples (e.g., $n = 6$) with known concentrations are measured and the signal is recorded. A sample with no chemical in it, called a blank, is also measured. (You have to be careful to define exactly what you mean by a blank. A blank could mean a container from the lab that has nothing in it but is prepared in a similar fashion to containers with actual samples in them. Or it could mean a field blank: the container was taken out to the field and subjected to the same process that all other containers were subjected to, except a physical sample of soil or water was not placed in the container.) Usually, replicate measures at the same known concentrations are taken. (The term replicate must be well defined to distinguish between for example the same physical samples that are measured more than once vs. two different physical samples of the same known concentration.) The function calibrate initially fits a linear calibration line or curve. Once the calibration line is fit, samples with unknown concentrations are measured and their signals are recorded. In order to produce estimated concentrations, you have to use inverse regression to map the signals to the estimated concentrations. We can quantify the uncertainty in the estimated concentration by combining inverse regression with prediction limits for the signal $S$.

References

Currie, L.A. (1997). Detection: International Update, and Some Emerging Di-Lemmas Involving Calibration, the Blank, and Multiple Detection Decisions. Chemometrics and Intelligent Laboratory Systems 37, 151--181. Draper, N., and H. Smith. (1998). Applied Regression Analysis. Third Edition. John Wiley and Sons, New York, Chapter 3 and p.335. Hubaux, A., and G. Vos. (1970). Decision and Detection Limits for Linear Calibration Curves. Annals of Chemistry 42, 849--855. Millard, S.P., and N.K. Neerchal. (2001). Environmental Statistics with S-PLUS. CRC Press, Boca Raton, FL, pp.562--575.

See Also

pointwise, calibrate, detectionLimitCalibrate, lm

Examples

# The data frame EPA.97.cadmium.111.df contains calibration data 
  # for cadmium at mass 111 (ng/L) that appeared in 
  # Gibbons et al. (1997b) and were provided to them by the U.S. EPA.  
  # Here we 
  # 1. Display a plot of these data along with the fitted calibration 
  #    line and 99\% non-simultaneous prediction limits. 
  # 2. Then based on an observed signal of 60 from a sample with 
  #    unknown concentration, we use the calibration line to estimate 
  #    the true concentration and use the prediction limits to compute 
  #    confidence bounds for the true concentration. 
  # An observed signal of 60 results in an estimated value of cadmium 
  # of 59.97 ng/L and a confidence interval of [53.83, 66.15]. 
  # See Millard and Neerchal (2001, pp.566-569) for more details on 
  # this example.

  Cadmium <- EPA.97.cadmium.111.df$Cadmium 

  Spike <- EPA.97.cadmium.111.df$Spike 

  calibrate.list <- calibrate(Cadmium ~ Spike, 
    data = EPA.97.cadmium.111.df) 

  newdata <- data.frame(Spike = seq(min(Spike), max(Spike), 
    length.out = 100))

  pred.list <- predict(calibrate.list, newdata = newdata, se.fit = TRUE) 

  pointwise.list <- pointwise(pred.list, coverage = 0.99, 
    individual = TRUE)

  plot(Spike, Cadmium, ylim = c(min(pointwise.list$lower), 
    max(pointwise.list$upper)), xlab = "True Concentration (ng/L)", 
    ylab = "Observed Concentration (ng/L)") 

  abline(calibrate.list, lwd=2) 

  lines(newdata$Spike, pointwise.list$lower, lty=8, lwd=2) 

  lines(newdata$Spike, pointwise.list$upper, lty=8, lwd=2) 

  title(paste("Calibration Line and 99% Prediction Limits", 
    "for US EPA Cadmium 111 Data", sep = "")) 

 
  # Now estimate the true concentration based on 
  # an observed signal of 60 ng/L. 

  inversePredictCalibrate(calibrate.list, obs.y = 60, 
    intervals = TRUE, coverage = 0.99, individual = TRUE) 

  #     obs.y   pred.x   lpl.x    upl.x 
  #[1,]    60 59.97301 53.8301 66.15422 
  #attr(, "coverage"): 
  #[1] 0.99 
  #attr(, "simultaneous"): 
  #[1] FALSE

  rm(Cadmium, Spike, calibrate.list, newdata, pred.list, pointwise.list)