nls {nls} | R Documentation |
Determine the nonlinear least squares estimates of the parameters.
nls(formula, data, start, control = nls.control(), algorithm = "default", trace = FALSE, subset, na.action)
formula |
a nonlinear model formula including variables and parameters |
data |
an optional data frame in which to evaluate the variables in
formula |
start |
a named list or named numeric vector of starting estimates |
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. The other alternative is "plinear", the Golub-Pereyra algorithm for partially linear least-squares models. |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
na.action |
a function which indicates what should happen
when the data contain NA s. |
An nls
object is a type of fitted model object. It has methods
for the generic functions coef
, formula
, resid
,
print
, summary
, and fitted
.
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. |
Douglas M. Bates and Saikat DebRoy
Bates, D.M. and Watts, D.G. (1988), Nonlinear Regression Analysis and Its Applications, Wiley
library( nls ) data( DNase ) DNase1 <- DNase[ DNase$Run == 1, ] ## using a selfStart model fm1DNase1 <- nls( density ~ SSlogis( log(conc), Asym, xmid, scal ), DNase1 ) 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 )