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Fit nonlinear growth models to growth data

Source: R/parametric_fits.R
growth.gcFitModel.Rd

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

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 time.

raw.data

Raw growth data provided to the function as data.

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 AIC.

nls

nls object for the chosen model generated by the nls function.

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 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.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'))
#> --> Try to fit model logistic
#> ....... OK
#> --> Try to fit model richards
#> ....... OK
#> --> Try to fit model gompertz
#> ....... OK
#> --> Try to fit model gompertz.exp
#> ... ERROR in nls(). For further information see help(growth.gcFitModel)
#> --> Try to fit model huang
#> .......... OK
#> --> Try to fit model baranyi
#> ........ OK
#> 
#> Best fitting model: ~baranyi

plot(TestFit, basesize = 18, eq.size = 1.5)
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.