growth.gcFit {QurvE} | R Documentation |
Perform a growth curve analysis on all samples in the provided dataset.
Description
growth.gcFit
performs all computational growth fitting operations based on the
user input.
Usage
growth.gcFit(time, data, control = growth.control(), parallelize = TRUE, ...)
Arguments
time |
(optional) A matrix containing time values for each sample. |
data |
Either...
|
control |
A |
parallelize |
Run linear fits and bootstrapping operations in parallel using all but one available processor cores |
... |
Further arguments passed to the shiny app. |
Value
A gcFit
object that contains all growth fitting results, compatible with
various plotting functions of the QurvE package.
raw.time |
Raw time matrix passed to the function as |
raw.data |
Raw growth dataframe passed to the function as |
gcTable |
Table with growth parameters and related statistics for each growth curve evaluation performed by the function. This table, which is also returned by the generic |
gcFittedLinear |
List of all |
gcFittedModels |
List of all |
gcFittedSplines |
List of all |
gcBootSplines |
List of all |
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 workflows:
flFit()
,
growth.workflow()
Other growth fitting functions:
growth.drFit()
,
growth.gcBootSpline()
,
growth.gcFitLinear()
,
growth.gcFitModel()
,
growth.gcFitSpline()
,
growth.workflow()
Other dose-response analysis functions:
flFit()
,
growth.drBootSpline()
,
growth.drFitSpline()
,
growth.workflow()
Examples
# Create random growth data set
rnd.data1 <- rdm.data(d = 35, mu = 0.8, A = 5, label = 'Test1')
rnd.data2 <- rdm.data(d = 35, mu = 0.6, A = 4.5, label = 'Test2')
rnd.data <- list()
rnd.data[['time']] <- rbind(rnd.data1$time, rnd.data2$time)
rnd.data[['data']] <- rbind(rnd.data1$data, rnd.data2$data)
# Run growth curve analysis workflow
res <- growth.gcFit(time = rnd.data$time,
data = rnd.data$data,
parallelize = FALSE,
control = growth.control(suppress.messages = TRUE,
fit.opt = 's'))