oneill_1972 {rTPC} | R Documentation |
O'Neill model for fitting thermal performance curves
Description
O'Neill model for fitting thermal performance curves
Usage
oneill_1972(temp, rmax, ctmax, topt, q10)
Arguments
temp |
temperature in degrees centigrade |
rmax |
maximum rate at optimum temperature |
ctmax |
high temperature (ºC) at which rates become negative |
topt |
optimum temperature (ºC) |
q10 |
defines the fold change in performance as a result of increasing the temperature by 10 ºC |
Details
Equation:
Start values in get_start_vals
are derived from the data and previous values in the literature
Limits in get_lower_lims
and get_upper_lims
are based on 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.
References
O’Neill, R.V., Goldstein, R.A., Shugart, H.H., Mankin, J.B. Terrestrial Ecosystem Energy Model. Eastern Deciduous Forest Biome Memo Report Oak Ridge. The Environmental Sciences Division of the Oak Ridge National Laboratory. (1972)
Examples
# load in ggplot
library(ggplot2)
# 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 = 'oneill_1972')
# fit model
mod <- nls.multstart::nls_multstart(rate~oneill_1972(temp = temp, rmax, ctmax, topt, q10),
data = d,
iter = c(4,4,4,4),
start_lower = start_vals - 10,
start_upper = start_vals + 10,
lower = get_lower_lims(d$temp, d$rate, model_name = 'oneill_1972'),
upper = get_upper_lims(d$temp, d$rate, model_name = 'oneill_1972'),
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()