| nls {stats} | R Documentation |
Determine the nonlinear (weighted) least-squares estimates of the parameters of a nonlinear model.
nls(formula, data, start, control, algorithm,
trace, subset, weights, na.action, model,
lower, upper, ...)
formula |
a nonlinear model formula including variables and parameters. Will be coerced to a formula if necessary. |
data |
an optional data frame in which to evaluate the variables in
formula. Can also be a list or an environment, but not a matrix. |
start |
a named list or named numeric vector of starting
estimates. Since R 2.4.0, when start is missing, a very
cheap guess for start is tried (if algorithm != "plinear").
|
control |
an optional list of control settings. See
nls.control for the names of the settable control
values and their effect. |
algorithm |
character string specifying the algorithm to use.
The default algorithm is a Gauss-Newton algorithm. Other possible
values are "plinear" for the Golub-Pereyra algorithm for
partially linear least-squares models and "port" for the
‘nl2sol’ algorithm from the Port package. |
trace |
logical value indicating if a trace of the iteration
progress should be printed. Default is FALSE. If
TRUE the residual (weighted) sum-of-squares and the
parameter values are printed at the conclusion of each iteration.
When the "plinear" algorithm is used, the conditional
estimates of the linear parameters are printed after the nonlinear
parameters. When the "port" algorithm is used the
objective function value printed is half the residual (weighted)
sum-of-squares. |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
weights |
an optional numeric vector of (fixed) weights. When present, the objective function is weighted least squares. |
na.action |
a function which indicates what should happen
when the data contain NAs. The default is set by
the na.action setting of options, and is
na.fail if that is unset. The “factory-fresh”
default is na.omit. Value na.exclude
can be useful. |
model |
logical. If true, the model frame is returned as part of
the object. Default is FALSE. |
lower, upper |
vectors of lower and upper bounds, replicated to
be as long as start. If unspecified, all parameters are
assumed to be unconstrained. Bounds can only be used with the
"port" algorithm. They are ignored, with a warning, if given
for other algorithms. |
... |
Additional optional arguments. None are used at present. |
An nls object is a type of fitted model object. It has methods
for the generic functions anova, coef,
confint, deviance,
df.residual, fitted,
formula, logLik, predict,
print, profile, residuals,
summary, vcov and weights.
Variables in formula are looked for first in data, then
the environment of formula and finally along the search path.
Functions in formula are searched for first in the environment
of formula and then along the search path.
A list of
m |
an nlsModel object incorporating the model. |
data |
the expression that was passed to nls as the data
argument. The actual data values are present in the environment of
the m component. |
call |
the matched call with several components, notably
algorithm. |
na.action |
the "na.action" attribute (if any) of the
model frame. |
dataClasses |
the "dataClasses" attribute (if any) of the
"terms" attribute of the model frame. |
model |
if model = TRUE, the model frame. |
weights |
if weights is supplied, the weights. |
convInfo |
when algorithm is not "port", a list with
convergence information. |
control |
the control list used, see the control
argument. |
convergence, message |
for an algorithm = "port" fit only,
a convergence code (0 for convergence) and message.
|
Note that setting warnOnly = TRUE in the control
argument (see nls.control) returns a non-converged
object (since R version 2.5.0) which might be useful for further
convergence analysis, but not for inference.
Do not use nls on artificial "zero-residual" data.
The nls function uses a relative-offset convergence criterion
that compares the numerical imprecision at the current parameter
estimates to the residual sum-of-squares. This performs well on data of
the form
y = f(x, theta) + eps
(with
var(eps) > 0). It fails to indicate convergence on data of the form
y = f(x, theta)
because the criterion amounts to
comparing two components of the round-off error. If you wish to test
nls on artificial data please add a noise component, as shown
in the example below.
The algorithm = "port" code appears unfinished, and does
not even check that the starting value is within the bounds.
Use with caution, especially where bounds are supplied.
Douglas M. Bates and Saikat DebRoy
Bates, D. M. and Watts, D. G. (1988) Nonlinear Regression Analysis and Its Applications, Wiley
Bates, D. M. and Chambers, J. M. (1992) Nonlinear models. Chapter 10 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
http://netlib.bell-labs.com/netlib/port/ for the Port library documentation.
summary.nls, predict.nls,
profile.nls.
DNase1 <- subset(DNase, Run == 1)
## using a selfStart model
fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1)
summary(fm1DNase1)
## the coefficients only:
coef(fm1DNase1)
## including their SE, etc:
coef(summary(fm1DNase1))
## using conditional linearity
fm2DNase1 <- nls(density ~ 1/(1 + exp((xmid - log(conc))/scal)),
data = DNase1,
start = list(xmid = 0, scal = 1),
alg = "plinear", trace = TRUE)
summary(fm2DNase1)
## without conditional linearity
fm3DNase1 <- nls(density ~ Asym/(1 + exp((xmid - log(conc))/scal)),
data = DNase1,
start = list(Asym = 3, xmid = 0, scal = 1),
trace = TRUE)
summary(fm3DNase1)
## using Port's nl2sol algorithm
fm4DNase1 <- nls(density ~ Asym/(1 + exp((xmid - log(conc))/scal)),
data = DNase1,
start = list(Asym = 3, xmid = 0, scal = 1),
trace = TRUE, algorithm = "port")
summary(fm4DNase1)
## weighted nonlinear regression
Treated <- Puromycin[Puromycin$state == "treated", ]
weighted.MM <- function(resp, conc, Vm, K)
{
## Purpose: exactly as white book p. 451 -- RHS for nls()
## Weighted version of Michaelis-Menten model
## ----------------------------------------------------------
## Arguments: 'y', 'x' and the two parameters (see book)
## ----------------------------------------------------------
## Author: Martin Maechler, Date: 23 Mar 2001
pred <- (Vm * conc)/(K + conc)
(resp - pred) / sqrt(pred)
}
Pur.wt <- nls( ~ weighted.MM(rate, conc, Vm, K), data = Treated,
start = list(Vm = 200, K = 0.1),
trace = TRUE)
summary(Pur.wt)
## The two examples below show that you can fit a model to
## artificial data with noise but not to artificial data
## without noise.
x <- 1:10
y <- 2*x + 3 # perfect fit
yeps <- y + rnorm(length(y), sd = 0.01) # added noise
nls(yeps ~ a + b*x, start = list(a = 0.12345, b = 0.54321),
trace = TRUE)
## Not run:
## terminates in an error, because convergence cannot be confirmed:
nls(y ~ a + b*x, start = list(a = 0.12345, b = 0.54321),
trace = TRUE)
## End(Not run)
## nls() internal very cheap guess for starting values can be sufficient:
x <- -(1:100)/10
y <- 100 + 10 * exp(x / 2) + rnorm(x)/10
nlmod <- nls(y ~ Const + A * exp(B * x), trace=TRUE)
plot(x,y, main = "nls(*), data, true function and fit, n=100")
curve(100 + 10 * exp(x / 2), col=4, add = TRUE)
lines(x, predict(nlmod), col=2)