growth.gcBootSpline {QurvE} | 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 |
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 |
raw.data |
Raw growth data provided to the function as |
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 |
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 |
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)