View source: R/betatypesimplified_2008.R
betatypesimplified_2008 | R Documentation |
Simplified Beta-type model for fitting thermal performance curves
betatypesimplified_2008(temp, rho, alpha, beta)
temp |
temperature in degrees centigrade |
rho |
dimensionless parameter |
alpha |
dimensionless parameter |
beta |
dimensionless parameter |
Equation:
rate = \rho \cdot \left(a - \frac{T}{10}\right) \cdot \left(\frac{T}{10}\right)^b
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.
a numeric vector of rate values based on the temperatures and parameter values provided to the function
Generally we found this model easy to fit.
Francis Windram
Damos, P. & Savopoulou-Soultani, M. Temperature-dependent bionomics and modeling of Anarsia lineatella (Lepidoptera: Gelechiidae) in the laboratory. J. Econ. Entomol. 101, 1557–1567 (2008).
# 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 = 'betatypesimplified_2008')
# fit model
mod <- nls.multstart::nls_multstart(rate~betatypesimplified_2008(temp = temp, rho, alpha, beta),
data = d,
iter = c(7,7,7),
start_lower = start_vals - 10,
start_upper = start_vals + 10,
lower = get_lower_lims(d$temp, d$rate, model_name = 'betatypesimplified_2008'),
upper = get_upper_lims(d$temp, d$rate, model_name = 'betatypesimplified_2008'),
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()
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