growth.gcFitModel {QurvE} | R Documentation |
Fit nonlinear growth models to growth data
Description
growth.gcFitModel
determines a parametric growth model that best describes the data.
Usage
growth.gcFitModel(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 gcFitModel
object that contains physiological parameters and information about the best fit. Use plot.gcFitModel
to visualize the parametric fit and growth equation.
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. |
fit.time |
Fitted time values. |
fit.data |
Fitted growth values. |
parameters |
List of determined growth parameters. |
-
A
: Maximum growth. -
dY
: Difference in maximum growth and minimum growth of the fitted model. -
mu
: Maximum growth rate (i.e., maximum in first derivative of the spline). -
lambda
: Lag time. -
b.tangent
: Intersection of the tangent at the maximum growth rate with the abscissa. -
fitpar
: For some models: list of additional parameters used in the equations describing the growth curve. -
integral
: Area under the curve of the parametric fit.
model |
(Character) The model that obtained the fit with the lowest AIC, determined by |
nls |
|
reliable |
(Logical) Indicates whether the performed fit is reliable (to be set manually). |
fitFlag |
(Logical) Indicates whether a parametric model was successfully fitted on the data. |
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.gcBootSpline()
,
growth.gcFitLinear()
,
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
# Perform parametric fit
TestFit <- growth.gcFitModel(time, data, gcID = 'TestFit',
control = growth.control(fit.opt = 'm'))
plot(TestFit, basesize = 18, eq.size = 1.5)