weight_select {envalysis}

Tools for weighted calibrations

Description

Selecting optimum model weights by comparing sum relative errors, this is relerr(), of weighted calibration() models as suggested by Almeida et al. (2002).

Usage

weight_select(x, weights, ...)

## S3 method for class 'calibration'
weight_select(x, weights = NULL, ...)

relerr(x)

## S3 method for class 'calibration'
relerr(x)

Arguments

x

an object of class 'calibration'.

weights

a list of weights to be added to the default weights to be checked. These are 1/concentration^0.5, 1/concentration^1, 1/concentration^2, 1/signal^0.5, 1/signal^1, and 1/signal^2.

...

further arguments passed to calibration().

Details

If calibration data is not homoscedastic, a weighted least squares linear calibration model may be applied to counteract the influence of high concentrations on the regression model. This, in turn, typically improves the accuracy at the lower end of the calibration curve (Almeida et al., 2002). weight_select uses sum relative errors (relerr) to find the best weight as suggested by Almeida et al. (2002). Predefined weights include 1/concentration^0.5, 1/concentration^1, 1/concentration^2, 1/signal^0.5, 1/signal^1, and 1/signal^2 (see calibration() for details).

Value

weight_select() produces a matrix with differently weighted 'calibration' models ordered by sum relative errors. relerr() compares the nominal concentrations with those predicted by the calibration model.

Author(s)

Julius Albert, Kilian Kenngott, Zacharias Steinmetz

References

Almeida, A.M.D., Castel-Branco, M.M., & Falcao, A.C. (2002). Linear regression for calibration lines revisited: weighting schemes for bioanalytical methods. Journal of Chromatography B, 774(2), 215-222. doi:10.1016/S1570-0232(02)00244-1.

See Also

Other calibration: calibration(), din32645, icp, matrix_effect(), neitzel2003, phenolics

Examples

data(din32645)
din <- calibration(Area ~ Conc, data = din32645)

weight_select(din)

relerr(din)