lactin2_1995 {rTPC} | R Documentation |
Lactin2 model for fitting thermal performance curves
Description
Lactin2 model for fitting thermal performance curves
Usage
lactin2_1995(temp, a, b, tmax, delta_t)
Arguments
temp |
temperature in degrees centigrade |
a |
constant that determines the steepness of the rising portion of the curve |
b |
constant that determines the height of the overall curve |
tmax |
the temperature at which the curve begins to decelerate beyond the optimum (ºC) |
delta_t |
thermal safety margin (ºC) |
Details
Equation:
rate= = exp^{a \cdot temp} - exp^{a \cdot t_{max} - \bigg(\frac{t_{max} - temp}{\delta _{t}}\bigg)} + 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.
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
Lactin, D.J., Holliday, N.J., Johnson, D.L. & Craigen, R. Improved rate models of temperature-dependent development by arthropods. Environmental Entomology 24, 69-75 (1995)
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 = 'lactin2_1995')
# fit model
mod <- nls.multstart::nls_multstart(rate~lactin2_1995(temp = temp, a, b, tmax, delta_t),
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 = 'lactin2_1995'),
upper = get_upper_lims(d$temp, d$rate, model_name = 'lactin2_1995'),
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()