nlsList: List of nls Objects with a Common Model

Description

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.

Usage

nlsList(model, data, start, control, level, subset,
        na.action = na.fail, pool = TRUE, warn.nls = NA)
# S3 method for formula
nlsList(model, data, start, control, level, subset,
        na.action = na.fail, pool = TRUE, warn.nls = NA)
# S3 method for nlsList
update(object, model., ..., evaluate = TRUE)

Value

a list of 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.

Arguments

object: an object inheriting from class nlsList, representing a list of fitted nls objects.
model: either a nonlinear model formula, with the response on the left of a ~ operator and an expression involving parameters, covariates, and a grouping factor separated by the | operator on the right, or a selfStart function. The method function nlsList.selfStart is documented separately.
model.: changes to the model -- see update.formula for details.
data: a data frame in which to interpret the variables named in model.
start: an optional named list with initial values for the parameters to be estimated in model. It is passed as the start argument to each nls call and is required when the nonlinear function in model does not inherit from class selfStart.
control: a list of control values passed as the control argument to nls. Defaults to an empty list.
level: an optional integer specifying the level of grouping to be used when multiple nested levels of grouping are present.
subset: an optional expression indicating the subset of the rows of 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 the row names to be included. All observations are included by default.
na.action: a function that indicates what should happen when the data contain NAs. The default action (na.fail) causes nlsList to print an error message and terminate if there are any incomplete observations.
pool: an optional logical value that is preserved as an attribute of the returned value. This will be used as the default for pool in calculations of standard deviations or standard errors for summaries.
warn.nls: logical indicating if nls() errors (all of which are caught by tryCatch) should be signalled as a “summarizing” warning.
...: some methods for this generic require additional arguments. None are used in this method.
evaluate: If TRUE evaluate the new call else return the call.

Details

As nls(.) is called on each sub group, and convergence of these may be problematic, these calls happen with error catching.

Since nlme version 3.1-127 (2016-04), all the errors are caught (via tryCatch) and if present, a “summarizing” warning is stored as attribute of the resulting "nlsList" object and signalled unless suppressed by warn.nls = FALSE or currently also when warn.nls = NA (default) and getOption("show.error.messages") is false.

nlsList() originally had used try(*) (with its default silent=FALSE) and hence all errors were printed to the console unless the global option show.error.messages was set to true. This still works, but has been deprecated.

References

Pinheiro, J.C., and Bates, D.M. (2000), Mixed-Effects Models in S and S-PLUS, Springer.

Examples

Run this code

fm1 <- nlsList(uptake ~ SSasympOff(conc, Asym, lrc, c0),
   data = CO2, start = c(Asym = 30, lrc = -4.5, c0 = 52))
summary(fm1)
cfm1 <- confint(fm1) # via profiling each % FIXME: only *one* message instead of one *each*
mat.class <- class(matrix(1)) # ("matrix", "array") for R >= 4.0.0;  ("matrix" in older R)
i.ok <- which(vapply(cfm1,
                function(r) identical(class(r), mat.class), NA))
stopifnot(length(i.ok) > 0, !anyNA(match(c(2:4, 6:9, 12), i.ok)))
## where as (some of) the others gave errors during profile re-fitting :
str(cfm1[- i.ok])

Run the code above in your browser using DataLab