Description Usage Arguments Details Value Author(s) References See Also Examples
nlsLM
is a modified version of nls
that uses nls.lm
for fitting.
Since an object of class 'nls' is returned, all generic functions such as anova
,
coef
, confint
, deviance
, df.residual
,
fitted
, formula
, logLik
, predict
,
print
, profile
, residuals
, summary
,
update
, vcov
and weights
are applicable.
1 2 3 4 
formula 
a nonlinear model 
data 
an optional data frame in which to evaluate the variables in 
start 
a named list or named numeric vector of starting estimates. 
jac 
A function to return the Jacobian. 
algorithm 
only method 
control 
an optional list of control settings. See 
lower 
A numeric vector of lower bounds on each parameter. If not given, the default lower bound for each parameter is set to 
upper 
A numeric vector of upper bounds on each parameter. If not given, the default upper bound for each parameter is set to 
trace 
logical value indicating if a trace of the iteration progress should be printed. Default is 
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. See the

na.action 
a function which indicates what should happen when the data contain 
model 
logical. If true, the model frame is returned as part of the object. Default is 
... 
Additional optional arguments. None are used at present. 
The standard nls
function was modified in several ways to incorporate the LevenbergMarquardt type nls.lm
fitting algorithm. The formula
is transformed into a function that returns a vector of (weighted) residuals whose sum square is minimized by nls.lm
. The optimized parameters are then transferred
to nlsModel
in order to obtain an object of class 'nlsModel'. The internal C function C_nls_iter
and nls_port_fit
were removed to avoid subsequent "GaussNewton", "port" or "plinear" types of optimization of nlsModel
. Several other small modifications were made in order to make all generic functions work on the output.
A list of
m 
an 
data 
the expression that was passed to 
call 
the matched call. 
convInfo 
a list with convergence information. 
control 
the control 
na.action 
the 
dataClasses 
the 
model 
if 
weights 
if 
AndrejNikolai Spiess and Katharine M. Mullen
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.
J.J. More, "The LevenbergMarquardt algorithm: implementation and theory," in Lecture Notes in Mathematics 630: Numerical Analysis, G.A. Watson (Ed.), SpringerVerlag: Berlin, 1978, pp. 105116.
nls.lm
, nls
, nls.lm.control
, optim
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84  ### Examples from 'nls' doc ###
DNase1 < subset(DNase, Run == 1)
## using a selfStart model
fm1DNase1 < nlsLM(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1)
## using logistic formula
fm2DNase1 < nlsLM(density ~ Asym/(1 + exp((xmid  log(conc))/scal)),
data = DNase1,
start = list(Asym = 3, xmid = 0, scal = 1))
## all generics are applicable
coef(fm1DNase1)
confint(fm1DNase1)
deviance(fm1DNase1)
df.residual(fm1DNase1)
fitted(fm1DNase1)
formula(fm1DNase1)
logLik(fm1DNase1)
predict(fm1DNase1)
print(fm1DNase1)
profile(fm1DNase1)
residuals(fm1DNase1)
summary(fm1DNase1)
update(fm1DNase1)
vcov(fm1DNase1)
weights(fm1DNase1)
## weighted nonlinear regression using
## inverse squared variance of the response
## gives same results as original 'nls' function
Treated < Puromycin[Puromycin$state == "treated", ]
var.Treated < tapply(Treated$rate, Treated$conc, var)
var.Treated < rep(var.Treated, each = 2)
Pur.wt1 < nls(rate ~ (Vm * conc)/(K + conc), data = Treated,
start = list(Vm = 200, K = 0.1), weights = 1/var.Treated^2)
Pur.wt2 < nlsLM(rate ~ (Vm * conc)/(K + conc), data = Treated,
start = list(Vm = 200, K = 0.1), weights = 1/var.Treated^2)
all.equal(coef(Pur.wt1), coef(Pur.wt2))
## 'nlsLM' can fit zeronoise data
## in contrast to 'nls'
x < 1:10
y < 2*x + 3
## Not run:
nls(y ~ a + b * x, start = list(a = 0.12345, b = 0.54321))
## End(Not run)
nlsLM(y ~ a + b * x, start = list(a = 0.12345, b = 0.54321))
### Examples from 'nls.lm' doc
## values over which to simulate data
x < seq(0,5, length = 100)
## model based on a list of parameters
getPred < function(parS, xx) parS$a * exp(xx * parS$b) + parS$c
## parameter values used to simulate data
pp < list(a = 9,b = 1, c = 6)
## simulated data with noise
simDNoisy < getPred(pp, x) + rnorm(length(x), sd = .1)
## make model
mod < nlsLM(simDNoisy ~ a * exp(b * x) + c,
start = c(a = 3, b = 0.001, c = 1),
trace = TRUE)
## plot data
plot(x, simDNoisy, main = "data")
## plot fitted values
lines(x, fitted(mod), col = 2, lwd = 2)
## create declining cosine
## with noise
TT < seq(0, 8, length = 501)
tau < 2.2
N0 < 1000
a < 0.25
f0 < 8
Ndet < N0 * exp(TT/tau) * (1 + a * cos(f0 * TT))
N < Ndet + rnorm(length(Ndet), mean = Ndet, sd = .01 * max(Ndet))
## make model
mod < nlsLM(N ~ N0 * exp(TT/tau) * (1 + a * cos(f0 * TT)),
start = c(tau = 2.2, N0 = 1500, a = 0.25, f0 = 10),
trace = TRUE)
## plot data
plot(TT, N, main = "data")
## plot fitted values
lines(TT, fitted(mod), col = 2, lwd = 2)

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