growth.drBootSpline {QurvE}

Perform a smooth spline fit on response vs. concentration data of a single sample

Description

growth.drBootSpline resamples the values in a dataset with replacement and performs a spline fit for each bootstrap sample to determine the EC50.

Usage

growth.drBootSpline(conc, test, drID = "undefined", control = growth.control())

Arguments

conc

Vector of concentration values.

test

Vector of response parameter values of the same length as conc.

drID

(Character) The name of the analyzed sample.

control

A grofit.control object created with growth.control, defining relevant fitting options.

Value

An object of class drBootSpline containing a distribution of growth parameters and a drFitSpline object for each bootstrap sample. Use plot.drBootSpline to visualize all bootstrapping splines as well as the distribution of EC50.

raw.conc

Raw data provided to the function as conc.

raw.test

Raw data for the response parameter provided to the function as test.

drID

(Character) Identifies the tested condition.

boot.conc

Table of concentration values per column, resulting from each spline fit of the bootstrap.

boot.test

Table of response values per column, resulting from each spline fit of the bootstrap.

boot.drSpline

List containing all drFitSpline objects generated by the call of growth.drFitSpline.

ec50.boot

Vector of estimated EC50 values from each bootstrap entry.

ec50y.boot

Vector of estimated response at EC50 values from each bootstrap entry.

BootFlag

(Logical) Indicates the success of the bootstrapping operation.

control

Object of class grofit.control containing list of options passed to the function as control.

References

Matthias Kahm, Guido Hasenbrink, Hella Lichtenberg-Frate, Jost Ludwig, Maik Kschischo (2010). grofit: Fitting Biological Growth Curves with R. Journal of Statistical Software, 33(7), 1-21. DOI: 10.18637/jss.v033.i07

See Also

Other dose-response analysis functions: flFit(), growth.drFitSpline(), growth.gcFit(), growth.workflow()

Examples

conc <- c(0, rev(unlist(lapply(1:18, function(x) 10*(2/3)^x))),10)
response <- c(1/(1+exp(-0.7*(4-conc[-20])))+rnorm(19)/50, 0)

TestRun <- growth.drBootSpline(conc, response, drID = 'test',
               control = growth.control(log.x.dr = TRUE, smooth.dr = 0.8,
                                        nboot.dr = 50))

print(summary(TestRun))
plot(TestRun, combine = TRUE)