calib {calib} | R Documentation |
Computes the calibration statistics
calib(calib.fit.out, y0, conf = 0.9, dilution = 1, samp.names, truth, times, samp.units = "", dose.units = "", dose.name = "", maxit = 1000, toler = 1e-05, rname = "response", extrap = F, xname = x)
calib.fit.out |
Output from calib.fit |
y0 |
~~Describe y0 here~~ |
conf |
A vector of mean response values to predict the unknown x |
dilution |
Dilution factor |
samp.names |
Names of the unknowns |
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, Daniel Samarov, Elaine McVey
Haaland and Davidian 1992
data(ELISA) data(experimentData) attach(ELISA) attach(experimentData) model <- calib.fit(Concentration, Response) cal <- calib(model, OD)