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 model
control
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 thNA
s. 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)
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