stinner_1974: Stinner model for fitting thermal performance curves

In padpadpadpad/rTPC: Fitting and Analysing Thermal Performance Curves

Stinner model for fitting thermal performance curves

Description

Stinner model for fitting thermal performance curves

Usage

stinner_1974(temp, rmax, topt, a, b)

Arguments

temp	temperature in degrees centigrade
rmax	the maximum rate
topt	optimum temperature (ºC) at which rates are maximal
a	dimensionless parameter
b	dimensionless parameter

Details

Equation:

\textrm{if} \quad temp <= t_{opt}: rate = rmax \cdot \frac{1 + exp^{a + b \cdot t_{opt}}}{(1 + exp^{a + b \cdot temp}}

\textrm{if} \quad temp <= t_{opt}: rate = rmax \cdot \frac{1 + exp^{a + b \cdot t_{opt}}}{(1 + exp^{a + b \cdot (2 \cdot t_{opt} - temp)}}

Start values in get_start_vals are derived from the data or sensible values from the literature.

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

Stinner, R. E., Gutierrez, A. P., & Butler Jr, G. D. (1974). An algorithm for temperature-dependent growth rate simulation12. The Canadian Entomologist, 106(5), 519-524.

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 = 'stinner_1974')
# fit model
mod <- nls.multstart::nls_multstart(rate~stinner_1974(temp = temp, rmax, topt, a, b),
data = d,
iter = c(5,5,5,5),
start_lower = start_vals - 10,
start_upper = start_vals + 10,
lower = get_lower_lims(d$temp, d$rate, model_name = 'stinner_1974'),
upper = get_upper_lims(d$temp, d$rate, model_name = 'stinner_1974'),
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()

padpadpadpad/rTPC documentation built on Feb. 21, 2025, 5:30 a.m.