gcFitSpline
, gcFitModel
and gcBootSpline
to generate estimates for growth parameters and respecting statistics.
gcFit(time, data, control = grofit.control())
time
. Format convention: 1. column, character as an experiment identifier; 2. column: character, additional information about respecting experiment; 3. column: concentration of substrate of a compound under which the experiment is obtained; 4.-(n+3). column: growth data corresponding to the time points in time
.
grofit.control
containing a list of options generated by the function grofit.control
.
gcFit
time
.data
.summary.gcFit
method applied to an gcFit
object, is used as an input for drFit
. 1. column: character, naming the experiment; 2. column: logical, indicating wether a logarithmic transformation was applied to concentration data; 3. column, logical, indicating wether a logarithmic transformation was applied to growth parameter; 4. column, numeric, number of bootstrap samples used; 5.-8. column: numeric, estimated EC50 value and respecting reponse value (5.-6.), also in original scale if a transformation is applied (7.-8.); 9.-10. column: numeric, mean and standard dev. from bootstrap sample; 11.-14. column: 90 and 95 percent confidence interval from bootstrap sample; 15. column: mean EC50 of bootstrap sample in original scale; 16.-19. 90 and 95 percent confidence interval in original scale.gcFitModel
objects, generated by the call of gcFitModel
. Note: access to each object via double brace: gcFittedModels[[#n]].gcFitSpline
objects, generated by the call of gcFitSpline
. Note: access to each object via double brace: gcFittedSplines[[#n]].gcFitSpline
objects, generated by the call of gcFitSpline
. Note: access to each object via double brace: gcFittedSplines[[#n]].grofit.control
containing list of options passed to the function as control
.grofit
, grofit.time
, grofit.data
, ran.data
, summary.gcFit
, plot.gcFit
# generate random growth curve data sets,
# fulfilling the conventions described in arguments
foo <- ran.data(100, 25)
time <- foo$time
data <- foo$data
# fit dose response curves
result <- gcFit(time,data, control=grofit.control(fit.opt="b"))
print(summary(result))
plot(result)
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