crm.fit: Fit Concentration Response Model

In nCal: Nonlinear Calibration

Description

crm.fit can fit a constant or power variance function or log transform both sides.

Usage

crm.fit (formula, data, fit.4pl=FALSE, var.model=c("constant","power"),
    robust="mean", method=c("gls-pl","gnls","mle"), max.iter=50, reltol=1e-3, 
    gof.threshold=0.2, log.both.sides=FALSE, verbose=FALSE) 


## S3 method for class 'crm'
deviance(object, ...)
## S3 method for class 'crm'
print(x, ..., digits=3)
## S3 method for class 'crm'
lines(x, ...)
## S3 method for class 'crm'
coef(object, parameterization=c("cla","gh","ed50b","edb50"), ...)

Arguments

formula
data
fit.4pl	Boolean
var.model	string
robust	string
method	string
max.iter	number
digits	number
reltol	numeric
gof.threshold	numeric
verbose	Boolean
log.both.sides	Boolean, log transform both sides
object, x	crm object
parameterization	string, output parameterization
...	additional argument

Details

crm.fit implements an iterative method for estimating a model with power variance. method: gls-pl means GLS-PL (see reference) log.both.sides: transform both sides (see reference)

Value

An object of crm and drm type.

var.power

estimated power parameter in the power variance function

References

Fong, Y., Yu, X. (2014) Transformation Model Choice in Nonlinear Regression Analysis of Serial Dilution Assays, submitted

Examples

## Not run: 
dat.std=dat.QIL3[dat.QIL3$assay_id=="LMX001",]

# run 3 iter to save time for examples
fit.1=crm.fit(fi~expected_conc, dat.std, var.model="power", verbose=TRUE, max.iter=2)
fit.2=crm.fit(log(fi)~expected_conc, dat.std, verbose=TRUE)
fit.3=crm.fit(log(fi)~expected_conc, dat.std, var.model="power", verbose=TRUE, max.iter=2)

sapply(list(fit.1, fit.2, fit.3), coef)
fit.1$var.power
fit.2$var.power
fit.3$var.power

plot(fit.1, log="xy", type="all", lwd=3, pch="*")
lines(fit.2, expy=TRUE, col=2, lwd=3)
lines(fit.3, expy=TRUE, col=4, lty=2, lwd=3)
    

## End(Not run)

nCal documentation built on Sept. 13, 2021, 5:08 p.m.