calib: Calibration function

In calibFit: Statistical models and tools for assay calibration

Description

Computes the calibration statistics

Usage

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)

Arguments

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

Value

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

Author(s)

Perry Haaland, Elaine McVey, Daniel Samarov

References

Davidian and Haaland 1990

See Also

calib-class, calib.fit, calib.fit-class, plot

Examples

data(HPLC)
attach(HPLC)
model <- calib.fit(Concentration, Response)
calib(model, Concentration)

calibFit documentation built on May 2, 2019, 6:15 p.m.