growth.gcBootSpline: Perform a bootstrap on growth vs. time data followed by...
In QurvE: Robust and User-Friendly Analysis of Growth and Fluorescence Curves

growth.gcBootSpline

R Documentation

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

Description

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

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)

QurvE documentation built on May 29, 2024, 3 a.m.