lrf_1991 {rTPC} | R Documentation |
Lobry-Rosso-Flandros (LRF) model for fitting thermal performance curves
Description
Lobry-Rosso-Flandros (LRF) model for fitting thermal performance curves
Usage
lrf_1991(temp, rmax, topt, tmin, tmax)
Arguments
temp |
temperature in degrees centigrade |
rmax |
maximum rate at optimum temperature |
topt |
optimum temperature (ºC) |
tmin |
low temperature (ºC) at which rates become negative |
tmax |
high temperature (ºC) at which rates become negative |
Details
Equation:
rate= rmax \cdot \frac{(temp - t_{max}) \cdot (temp - t_{min})^2}{(t_{opt} - t_{min}) \cdot ((t_{opt} - t_{min}) \cdot (temp - t_{opt}) - (t_{opt} - t_{max}) \cdot (t_{opt} + t_{min} - 2 \cdot temp))}
Start values in get_start_vals
are derived from the data.
Limits in get_lower_lims
and get_upper_lims
are derived from the data or based extreme values that are unlikely to occur in ecological settings.
Value
a numeric vector of rate values based on the temperatures and parameter values provided to the function
Note
Generally we found this model easy to fit.
Author(s)
Daniel Padfield
References
Rosso, L., Lobry, J. R., & Flandrois, J. P. An unexpected correlation between cardinal temperatures of microbial growth highlighted by a new model. Journal of Theoretical Biology, 162(4), 447-463. (1993)
Examples
# load in ggplot
library(ggplot2)
library(nls.multstart)
# subset for the first TPC curve
data('chlorella_tpc')
d <- subset(chlorella_tpc, curve_id == 1)
# get start values and fit model
start_vals <- get_start_vals(d$temp, d$rate, model_name = 'sharpeschoolhigh_1981')
# fit model
mod <- nls_multstart(rate~lrf_1991(temp = temp, rmax, topt, tmin, tmax),
data = d,
iter = c(3,3,3,3),
start_lower = start_vals - 10,
start_upper = start_vals + 10,
lower = get_lower_lims(d$temp, d$rate, model_name = 'lrf_1991'),
upper = get_upper_lims(d$temp, d$rate, model_name = 'lrf_1991'),
supp_errors = 'Y',
convergence_count = FALSE)
# look at model fit
summary(mod)
# get predictions
preds <- data.frame(temp = seq(min(d$temp), max(d$temp), length.out = 100))
preds <- broom::augment(mod, newdata = preds)
# plot
ggplot(preds) +
geom_point(aes(temp, rate), d) +
geom_line(aes(temp, .fitted), col = 'blue') +
theme_bw()