calib {calib} | R Documentation |
Computes the calibration statistics
calib(x, y0, conf = 0.9, dilution = 1, samp.names, m = x@m, truth, times, samp.units = "", dose.units = "", dose.name = "", maxit = 1000, toler = 1e-05, rname = "response", extrap = FALSE, xname = x)
x |
Output from calib.fit. |
y0 |
Points to be calibrated. |
conf |
A vector of mean response values to predict the unknown x. |
dilution |
Dilution factor. |
samp.names |
Names of the unknowns. |
m |
Number of replicates. |
truth |
Optional argument to provide true concentrations if they are known |
times |
... |
samp.units |
Names of the unknowns |
dose.units |
Units of dose |
dose.name |
Name of dose |
maxit |
Maximum number of iterations to use in optimization |
toler |
Tolerance for optimization step |
rname |
This is the name of the reponse variable |
extrap |
Option to extrapoloate out of range values |
xname |
Names of concentrations |
Estimated.x |
Predicted values of x (for example concentration) |
PredStdErr |
The predicted standard errors of the estimated x's |
inver.low |
The estimate of the lower confidence limit for the predicted x's using inverse estimation |
inver.up |
The estimate of the upper confidence limit for the predicted x's using inverse estimation |
wald.low |
The estimate of the lower confidence limit for the predicted x's using Wald estimation |
wald.up |
The estimate of the upper confidence limit for the predicted x's using Wald estimation |
avg.response |
y0 values |
Perry Haaland, Elaine McVey, Daniel Samarov
Davidian and Haaland 1990
calib-class, calib.fit, calib.fit-class, plot
data(HPLC) attach(HPLC) model <- calib.fit(Concentration, Response) calib(model, Concentration)