Perform a bootstrap on growth vs. time data followed by spline fits for each resample

growth.gcBootSpline resamples the growth-time value pairs in a dataset with replacement and performs a spline fit for each bootstrap sample.

Usage

growth.gcBootSpline(time, data, gcID = "undefined", control = growth.control())

Arguments

time

Vector of the independent variable (usually: time).

data

Vector of dependent variable (usually: growth values).

gcID

(Character) The name of the analyzed sample.

control

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

Value

A gcBootSpline object containing a distribution of growth parameters and a gcFitSpline object for each bootstrap sample. Use plot.gcBootSpline

to visualize all bootstrapping splines as well as the distribution of physiological parameters.

raw.time

Raw time values provided to the function as time.

raw.data

Raw growth data provided to the function as data.

gcID

(Character) Identifies the tested sample.

boot.time

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

boot.data

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

boot.gcSpline

List of gcFitSpline object, created by growth.gcFitSpline for each resample of the bootstrap.

lambda

Vector of estimated lambda (lag time) values from each bootstrap entry.

mu

Vector of estimated mu (maximum growth rate) values from each bootstrap entry.

A

Vector of estimated A (maximum growth) values from each bootstrap entry.

integral

Vector of estimated integral 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 growth fitting functions: growth.drFit(), growth.gcFitLinear(), growth.gcFitModel(), growth.gcFitSpline(), growth.gcFit(), growth.workflow()

Examples

# Create random growth dataset
rnd.dataset <- rdm.data(d = 35, mu = 0.8, A = 5, label = 'Test1')

# Extract time and growth data for single sample
time <- rnd.dataset$time[1,]
data <- rnd.dataset$data[1,-(1:3)] # Remove identifier columns

# Introduce some noise into the measurements
data <- data + stats::runif(97, -0.01, 0.09)

# Perform bootstrapping spline fit
TestFit <- growth.gcBootSpline(time, data, gcID = 'TestFit',
              control = growth.control(fit.opt = 's', nboot.gc = 50))

plot(TestFit, combine = TRUE, lwd = 0.5)