Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
library(biogrowth)
library(tidyverse)
library(cowplot)
## -----------------------------------------------------------------------------
my_conditions <- data.frame(time = c(0, 50),
temperature = c(35, 35)
)
## -----------------------------------------------------------------------------
q0 <- 1e-4
mu_opt <- .5
my_primary <- list(mu_opt = mu_opt,
Nmax = 1e8,N0 = 1e2,
Q0 = q0)
sec_temperature <- list(model = "CPM",
xmin = 5, xopt = 35, xmax = 40, n = 2)
my_secondary <- list(temperature = sec_temperature)
## -----------------------------------------------------------------------------
my_times <- seq(0, 50, length = 1000)
## Do the simulation
dynamic_prediction <- predict_growth(environment = "dynamic",
my_times,
my_primary,
my_secondary,
my_conditions)
## -----------------------------------------------------------------------------
lambda <- Q0_to_lambda(q0, mu_opt)
primary_model <- list(model = "Baranyi",
logN0 = 2, logNmax = 8, mu = mu_opt, lambda = lambda)
static_prediction <- predict_growth(my_times, primary_model)
plot(static_prediction) +
geom_line(aes(x = time, y = logN), linetype = 2,
data = dynamic_prediction$simulation,
colour = "green")
## -----------------------------------------------------------------------------
max_time <- 100
c(15, 20, 25, 30, 35) %>% # Temperatures for the calculation
set_names(., .) %>%
map(., # Definition of constant temperature profile
~ data.frame(time = c(0, max_time),
temperature = c(., .))
) %>%
map(., # Growth simulation for each temperature
~ predict_growth(environment = "dynamic",
my_times,
my_primary,
my_secondary,
env_conditions = .,
logbase_mu = 10)
) %>%
imap_dfr(., # Extract the simulation
~ mutate(.x$simulation, temperature = .y)
) %>%
ggplot() +
geom_line(aes(x = time, y = logN, colour = temperature)) +
theme_cowplot()
## -----------------------------------------------------------------------------
my_primary <- list(mu_opt = mu_opt,
Nmax = 1e8,N0 = 1e2,
Q0 = q0)
sec_temperature <- list(model = "CPM",
xmin = 5, xopt = 35, xmax = 40, n = 2)
sec_pH <- list(model = "CPM",
xmin = 4, xopt = 7, xmax = 8, n = 2)
my_secondary_2 <- list(temperature = sec_temperature,
pH = sec_pH)
## -----------------------------------------------------------------------------
max_time <- 100
c(5, 5.5, 6, 6.5, 7, 7.5) %>% # pH values for the calculation
set_names(., .) %>%
map(., # Definition of constant temperature profile
~ tibble(time = c(0, max_time),
temperature = c(35, 35),
pH = c(., .))
) %>%
map(., # Growth simulation for each temperature
~ predict_growth(environment = "dynamic",
my_times,
my_primary,
my_secondary_2,
env_conditions = .,
logbase_mu = 10)
) %>%
imap_dfr(., # Extract the simulation
~ mutate(.x$simulation, pH = .y)
) %>%
ggplot() +
geom_line(aes(x = time, y = logN, colour = pH)) +
theme_cowplot()
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