Defines a 'calibration' object for the calculation of concentrations
from measurement signals including estimations for the limit of detection
(LOD) and limit of quantification (LOQ) in accordance with DIN 32645 (2008).
calibration(
formula,
data = NULL,
blanks = NULL,
weights = NULL,
model = "lm",
check_assumptions = TRUE,
...
)# S3 method for calibration
print(x, ...)
# S3 method for calibration
summary(object, ...)
# S3 method for calibration
plot(x, interval = "conf", level = 0.95, ...)
# S3 method for calibration
as.list(x, which = c("coef", "adj.r.squared", "lod", "loq", "blanks"), ...)
lod(x, ...)
# S3 method for default
lod(x, ...)
# S3 method for calibration
lod(x, blanks = NULL, alpha = 0.01, level = 0.05, ...)
loq(x, ...)
# S3 method for default
loq(x, ...)
# S3 method for calibration
loq(x, blanks = NULL, alpha = 0.01, k = 3, level = 0.05, maxiter = 10, ...)
# S3 method for calibration
predict(object, newdata = NULL, interval = "conf", ...)
inv_predict(x, ...)
# S3 method for default
inv_predict(x, ...)
# S3 method for calibration
inv_predict(x, y, below_lod = NULL, method = "analytic", ...)
calibration returns an object of class
'calibration'.
print() calls the function parameters together with the respective LOD
and LOQ.
summary() may be used to retrieve the summary of the underlying model.
plot() plots the respective calibration curve together with the
measurement values.
as.list() returns a named list.
lod() and loq() return a named vector with the LOD and LOQ
together with lower and upper confidence limits.
predict() returns a data.frame of predictions.
inv_predict() predicts/calculates analyte concentrations from signal
intensities.
model formula providing the recorded signal intensities with
respect to the nominal/specified analyte concentrations in the form of
signal ~ concentration or signal ~ concentration - 1; model
formulas are currently restricted to those forms.
an optional data frame containing the variables in the model.
a vector of numeric blank values overriding those automatically retrieved from calibration data.
an optional character string containing one or more model
variables, for example, in the form of "1/concentration^0.5" or
"1/signal" which is internally converted to a numeric vector and
passed to the fitting process of the selected model; see also
weight_select()
model class to be used for fitting; currently,
lm() and rlm() are supported.
automatically check for normality and
homoscedasticity of model residuals using shapiro.test()
and bptest(), respectively; only executed if
weights == NULL.
further arguments passed to submethods; for
instance, the respective model environment such as lm(),
print(), or plot().
an object of class 'calibration' with a model formula
as shown above.
type of interval plotted (can be abbreviated); see
predict() for details.
tolerance/confidence level; see predict()
and confint() for details.
character vector indicating the parameters to export; defaults
to c("coef", "adj.r.squared", "lod", "loq", "blanks").
numeric; error tolerance for the detection limit (critical value).
numeric; relative uncertainty for the limit of quantification
(1/beta).
a positive integer specifying the maximum number of iterations to calculate the LOQ.
a data frame in which to look for variables with which to
predict. If NULL, values are guessed; predict.lm()
for details.
numeric; the value to inverse predict.
value to be assigned if inverse prediction is below LOD;
defaults to "NULL" which keeps predicted values untouched. Other
options may be NA or 0.
character indicating the method used for inverse prediction;
defaults to "analytic".
Zacharias Steinmetz
The LOD is defined as the lowest quantity of a substance that can be
distinguished from the absence of that substance (blank value) within a given
confidence level (alpha). The LOQ is defined as the lowest quantity of
a substance that can be quantified/distinguished from another sample given
with respect to a defined confidence level (k).
If the data supplied to calibration contain more than one blank
value, namely measurements with a nominal/specified concentration of or close
to zero, the LOD and LOQ are calculated from the deviation of the blank
samples. This method is called "blank method" according to DIN 32645 (2008)
and supposed to be more accurate than the so-called "calibration method"
which will be used for the estimation of LOD and LOQ when data does
not contain zero concentration measurements.
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. tools:::Rd_expr_doi("10.1016/S1570-0232(02)00244-1").
Currie, L.A. (1999). Nomenclature in evaluation of analytical methods including detection and quantification capabilities: (IUPAC Recommendations 1995). Analytica Chimica Acta 391, 105-126.
DIN 32645 (2008). Chemical analysis - Decision limit, detection limit and determination limit under repeatability conditions - Terms, methods, evaluation. Technical standard. Deutsches Institut für Normung, Berlin.
Massart, D.L., Vandeginste, B.G., Buydens, L.M.C., Lewi, P.J., & Smeyers-Verbeke, J. (1997). Handbook of chemometrics and qualimetrics: Part A. Elsevier Science Inc.
invest() for alternative inverse prediction methods;
Other calibration:
din32645,
icp,
matrix_effect(),
neitzel2003,
phenolics,
weight_select()
data(din32645)
din <- calibration(Area ~ Conc, data = din32645)
print(din)
summary(din)
plot(din)
as.list(din)
lod(din)
loq(din)
predict(din)
inv_predict(din, 5000)
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