ashrafi2_2018: Ashrafi II model for fitting thermal performance curves

View source: R/ashrafi2_2018.R

ashrafi2_2018R Documentation

Ashrafi II model for fitting thermal performance curves

Description

Ashrafi II model for fitting thermal performance curves

Usage

ashrafi2_2018(temp, a, b, c)

Arguments

temp

temperature in degrees centigrade

a

dimensionless parameter

b

dimensionless parameter

c

dimensionless parameter

Details

Equation:

rate=a + b \cdot temp^{\frac{3}{2}} + c \cdot temp^{2}

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)

Francis Windram

References

Ashrafi, R. et al. Broad thermal tolerance is negatively correlated with virulence in an opportunistic bacterial pathogen. Evolutionary Applications 11, 1700–1714 (2018).

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 = 'ashrafi2_2018')
# fit model
mod <- nls.multstart::nls_multstart(rate~ashrafi2_2018(temp = temp, a, b, c),
data = d,
iter = c(4,4,4),
start_lower = start_vals - 10,
start_upper = start_vals + 10,
lower = get_lower_lims(d$temp, d$rate, model_name = 'ashrafi2_2018'),
upper = get_upper_lims(d$temp, d$rate, model_name = 'ashrafi2_2018'),
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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