Package 'nls.multstart'

Example metabolic thermal response curves

Description

A dataset containing example data of rates of photosynthesis and respiration of the phytoplankton Chlorella vulgaris. Instantaneous rates of metabolism were made across a range of assay temperatures to incorporate the entire thermal response of the populations. The dataset is the cleaned version so some datapoints have been omitted.

Usage

data("Chlorella_TRC")

Format

A data frame with 649 rows and 7 variables:

curve_id

a unique value for each separate curve

growth.temp

the growth temperature that the culture was maintained at before measurements were taken (degrees centigrade)

process

whether the cultures had been kept for a long time at their growth temperature (adaptation/~100 generations) or a short time (a measure of acclimation/~10 generations)

flux

whether the curve depicts respiration or gross photosynthesis

temp

the assay temperature at which the metabolic rate was measured (degrees centigrade)

K

the assay temperature in degrees Kelvin

ln.rate

the metabolic rate measured (micro mol O2 micro gram C-1 hr-1)

Source

Daniel Padfield

References

Padfield, D., Yvon-durocher, G., Buckling, A., Jennings, S. & Yvon-durocher, G. (2015). Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton, Ecology Letters, 19, 133-142.

Examples

data("Chlorella_TRC")
library(ggplot2)
ggplot(Chlorella_TRC) +
 geom_point(aes(temp, ln.rate, col = process)) +
 facet_wrap(~ growth.temp + flux)

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Finds the best fit of non-linear model based on AIC score

Description

Finds the best estimated model using non-linear least squares regression using nlsLM(). The best fit is determined using AIC scores.

Arguments

formula	a non-linear 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 formula and modelweights.
iter	number of combinations of starting parameters which will be tried . If a single value is provided, then a shotgun/random-search/lhs 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 iter will override convergence_count.
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 supp_errors = 'Y', then warning messages will be suppressed and no error messages from nlsLM will be shown, reducing the number of error messages printed while the model attempts to converge using poor starting parameters. We advise to only use supp_errors = 'Y' when confident in the bounds of your starting parameters.
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 FALSE, then all of the iterations will be fitted, and the best model selected. Note that convergence_count can only be used with a shotgun/random-search approach, and not with a gridstart approach. This argument will be ignored if a gridstart approach is specified by a vector input for iter.
control	specific control can be specified using nls.lm.control.
modelweights	Optional model weights for the nls. If data is specified, then this argument should be the name of the numeric weights vector within the data object.
lhstype	Method to use for Latin Hypercube Sampling using lhs. Choice of "random" (simple random lhs, fast), "improved" (lhs with optimised euclidean distance between points, medium speed), "maximin" (lhs with maximised minimum distance between points, medium speed), or "genetic" (lhs optimised to the S optimilaity criterion using a genetic algorithm, slow). If not set, a purely random (shotgun) approach is taken. Only used if iter is a single number.
...	Extra arguments to pass to nlsLM if necessary.

Value

returns a nls object of the best estimated model fit.

Note

Useful additional arguments for nlsLM include: na.action = na.omit, lower/upper = c() where these represent upper and lower boundaries for parameter estimates.

Author(s)

Daniel Padfield

Granville Matheson

Francis Windram

See Also

nlsLM for details on additional arguments to pass to the nlsLM function. lhs for details of Latin Hypercube Sampling

Examples

# load in data

data("Chlorella_TRC")
Chlorella_TRC_test <- Chlorella_TRC[Chlorella_TRC$curve_id == 1,]

# run nls_multstart()

# define the Sharpe-Schoolfield equation
schoolfield_high <- function(lnc, E, Eh, Th, temp, Tc) {
 Tc <- 273.15 + Tc
 k <- 8.62e-5
 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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