Description Arguments Value Note Author(s) See Also Examples
Finds the best estimated model using nonlinear least squares regression using nlsLM(). The best fit is determined using AIC scores.
formula 
a nonlinear model formula, with the response on the left of a ~ operator and an expression involving parameters on the right. 
data 
(optional) data.frame, list or environment in which to evaluate
the variables in 
iter 
number of combinations of starting parameters which will be tried
. If a single value is provided, then a shotgun/randomsearch approach will
be used to sample starting parameters from a uniform distribution within the
starting parameter bounds. If a vector of the same length as the number of
parameters is provided, then a gridstart approach will be used to define
each combination of that number of equally spaced intervals across each of
the starting parameter bounds respectively. Thus, c(5,5,5) for three fitted
parameters yields 125 model fits. Supplying a vector for 
start_lower 
lower boundaries for the start parameters. If missing, this will default to 1e+10. 
start_upper 
upper boundaries for the start parameters. If missing, this will default to 1e+10. 
supp_errors 
if 
convergence_count 
The number of counts that the winning model should be
undefeated for before it is declared the winner. This argument defaults to
100. If specified as 
control 
specific control can be specified using

modelweights 
Optional model weights for the nls. If 
... 
Extra arguments to pass to 
returns a nls object of the best estimated model fit.
Useful additional arguments for nlsLM
include:
na.action = na.omit
, lower/upper = c()
where these represent
upper and lower boundaries for parameter estimates.
Daniel Padfield
Granville Matheson
nlsLM
for details on additional arguments
to pass to the nlsLM function.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  # load in data
data("Chlorella_TRC")
Chlorella_TRC_test < Chlorella_TRC[Chlorella_TRC$curve_id == 1,]
# run nls_multstart()
# define the SharpeSchoolfield equation
schoolfield_high < function(lnc, E, Eh, Th, temp, Tc) {
Tc < 273.15 + Tc
k < 8.62e5
boltzmann.term < lnc + log(exp(E/k*(1/Tc  1/temp)))
inactivation.term < log(1/(1 + exp(Eh/k*(1/Th  1/temp))))
return(boltzmann.term + inactivation.term)
}
fits < nls_multstart(ln.rate ~ schoolfield_high(lnc, E, Eh, Th, temp = K, Tc = 20),
data = Chlorella_TRC_test,
iter = 500,
start_lower = c(lnc=10, E=0.1, Eh=0.5, Th=285),
start_upper = c(lnc=10, E=2, Eh=5, Th=330),
lower = c(lnc=10, E=0, Eh=0, Th=0),
supp_errors = 'Y')

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