Data is partitioned according to the levels of the grouping
factor defined in model and individual nls fits are
obtained for each data partition, using the model defined in
model.nlsList(model, data, start, control, level, subset, na.action, pool)
## S3 method for class 'nlsList':
update(object, model., \dots, evaluate = TRUE)nlsList, representing
a list of fitted nls objects.~ operator and an expression involving
parameters, covariates, and a grouping factor separated by the
| operator on the right, or a selfStart<update.formula for
details.model.model. It is passed as the
start argument to each nls call and is required when
the nonlinear function in modelcontrol
argument to nls. Defaults to an empty list.data that should be used in the fit. This can be a logical
vector, or a numeric vector indicating which observation numbers are
to be included, or a character vector of thNAs. The default action (na.fail) causes
nlsList to print an error message and terminate if there are any
incomplete observations.pool in
calculations of standard deviations or standard errors for summaries.TRUE evaluate the new call else return the call.nls objects with as many components as the number of
groups defined by the grouping factor. Generic functions such as
coef, fixed.effects, lme, pairs,
plot, predict, random.effects, summary,
and update have methods that can be applied to an nlsList
object.nls, nlme.nlsList.fm1 <- nlsList(uptake ~ SSasympOff(conc, Asym, lrc, c0),
data = CO2, start = c(Asym = 30, lrc = -4.5, c0 = 52))
summary(fm1)Run the code above in your browser using DataLab