View source: R/weight_select.R
weight_select | R Documentation |
Selecting optimum model weights by comparing sum relative errors, this is
relerr()
, of weighted calibration()
models as
suggested by Almeida et al. (2002).
weight_select(x, weights, ...)
## S3 method for class 'calibration'
weight_select(x, weights = NULL, ...)
relerr(x)
## S3 method for class 'calibration'
relerr(x)
x |
an object of class ' |
weights |
a list of weights to be added to the default weights to be
checked. These are |
... |
further arguments passed to |
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).
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.
Julius Albert, Kilian Kenngott, Zacharias Steinmetz
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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/S1570-0232(02)00244-1")}.
Other calibration:
calibration()
,
din32645
,
icp
,
matrix_effect()
,
neitzel2003
,
phenolics
data(din32645)
din <- calibration(Area ~ Conc, data = din32645)
weight_select(din)
relerr(din)
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