Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
library(biogrowth)
library(tidyverse)
## -----------------------------------------------------------------------------
primary_model_data()
## -----------------------------------------------------------------------------
my_model <- "Baranyi"
## -----------------------------------------------------------------------------
primary_model_data(my_model)$pars
## -----------------------------------------------------------------------------
primary_model <- list(model = my_model, logN0 = 1, logNmax = 7, mu = .2, lambda = 20)
## -----------------------------------------------------------------------------
my_times <- seq(0, 100, length = 1000)
## -----------------------------------------------------------------------------
my_prediction <- predict_growth(environment = "constant", my_times, primary_model)
## -----------------------------------------------------------------------------
my_prediction
## -----------------------------------------------------------------------------
coef(my_prediction)
## ---- fig.width=6-------------------------------------------------------------
plot(my_prediction)
## ---- fig.width=6-------------------------------------------------------------
plot(my_prediction,
label_y1 = "log10 of the population size",
label_x = "Time (years)",
line_size = 2,
line_col = "red",
line_type = "dotted")
## ---- fig.width=6-------------------------------------------------------------
plot(my_prediction) + theme_gray() + xlab("Time (years)")
## -----------------------------------------------------------------------------
time_to_size(my_prediction, 3)
## -----------------------------------------------------------------------------
time_to_size(my_prediction, 9)
## ---- fig.width = 6-----------------------------------------------------------
primary_model <- list(model = my_model, logN0 = 1, logNmax = 7, mu = .2, lambda = 20)
Q0 <- lambda_to_Q0(lambda = 20, mu = .2)
equivalent_pars <- list(model = my_model, N0 = 10^1, Nmax = 10^7, mu_opt = .2,
Q0 = Q0)
equivalent_prediction <- predict_growth(environment = "constant", my_times,
equivalent_pars)
plot(equivalent_prediction)
## ---- fig.width=6-------------------------------------------------------------
primary_model <- list(model = my_model, logN0 = 1, logNmax = 7, mu = .2, lambda = 20)
prediction_base_e <- predict_growth(environment = "constant", my_times,
primary_model,
logbase_logN = exp(1))
prediction_base_e
plot(prediction_base_e)
## ---- fig.width=6-------------------------------------------------------------
primary_model <- list(model = my_model, N0 = 1, Nmax = 1e7, mu = .2, lambda = 20)
prediction_base_e <- predict_growth(environment = "constant", my_times,
primary_model,
logbase_logN = exp(1))
plot(prediction_base_e)
## ---- fig.width=6-------------------------------------------------------------
primary_model <- list(model = my_model, logN0 = 1, logNmax = 7, mu = .2, lambda = 20)
prediction_mu_e <- predict_growth(environment = "constant", my_times,
primary_model,
logbase_mu = exp(1))
prediction_mu_e
plot(prediction_mu_e)
## ---- fig.width=6-------------------------------------------------------------
primary_model <- list(model = my_model, logN0 = 1, logNmax = 7,
mu = .2 * log(10),
lambda = 20)
prediction_mu_e <- predict_growth(environment = "constant", my_times,
primary_model,
logbase_mu = exp(1)
)
plot(prediction_mu_e)
## -----------------------------------------------------------------------------
print(prediction_base_e)
time_to_size(prediction_base_e, size = 5)
## -----------------------------------------------------------------------------
time_to_size(prediction_base_e, log10(exp(5)), logbase_logN = 10)
## -----------------------------------------------------------------------------
my_conditions <- tibble(Time = c(0, 5, 40),
temperature = c(20, 30, 35),
pH = c(7, 6.5, 5)
)
## -----------------------------------------------------------------------------
ggplot(my_conditions) +
geom_line(aes(x = Time, y = temperature))
## -----------------------------------------------------------------------------
ggplot(my_conditions) +
geom_line(aes(x = Time, y = pH))
## -----------------------------------------------------------------------------
my_primary <- list(mu_opt = .9,
Nmax = 1e8,
N0 = 1e0,
Q0 = 1e-3)
## -----------------------------------------------------------------------------
secondary_model_data()
## -----------------------------------------------------------------------------
secondary_model_data("Zwietering")$pars
## -----------------------------------------------------------------------------
sec_temperature <- list(model = "Zwietering",
xmin = 25,
xopt = 35,
n = 1)
## -----------------------------------------------------------------------------
sec_pH <- list(model = "CPM",
xmin = 5.5,
xopt = 6.5,
xmax = 7.5,
n = 2)
## -----------------------------------------------------------------------------
my_secondary <- list(
temperature = sec_temperature,
pH = sec_pH
)
## -----------------------------------------------------------------------------
my_times <- seq(0, 50, length = 1000)
## -----------------------------------------------------------------------------
dynamic_prediction <- predict_growth(environment = "dynamic",
my_times,
my_primary,
my_secondary,
my_conditions,
formula = . ~ Time
)
## -----------------------------------------------------------------------------
dynamic_prediction
## ---- fig.width=6-------------------------------------------------------------
plot(dynamic_prediction)
## ---- fig.width=6-------------------------------------------------------------
plot(dynamic_prediction,
add_factor = "temperature",
line_col2 = "steelblue",
line_col = "magenta",
label_y2 = "Temperature (ÂșC)")
## -----------------------------------------------------------------------------
time_to_size(dynamic_prediction, 3)
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