biogrowth (version 1.0.6)

fit_MCMC_growth: Fit growth models using MCMC

Description

The function fit_MCMC_growth() has been superseded by the top-level function fit_growth(), which provides a unified approach for growth modelling.

But, it can still fit a growth model to a data obtained under dynamic conditions using the one-step approach (MCMC algorithm).

Usage

fit_MCMC_growth(
  fit_data,
  env_conditions,
  starting_point,
  known_pars,
  sec_model_names,
  niter,
  ...,
  check = TRUE,
  formula = logN ~ time,
  logbase_mu = logbase_logN,
  logbase_logN = 10
)

Value

An instance of FitDynamicGrowthMCMC().

Arguments

fit_data

Tibble with the data to use for model fit. It must contain a column with the elapsed time (named "time" by default) and another one with the decimal logarithm of the observed population size (named "logN" by default). Different column names can be specified using the "formula" argument.

env_conditions

Tibble with the (dynamic) environmental conditions during the experiment. It must have one column with the elapsed time (named "time" by default) and as many columns as required with the environmental conditions. A different column name can be specified using the "formula" argument, although it must be the same one as in "fit_data". Note that only those defined in "sec_model_names" will be considered for the model fit.

starting_point

A named vector of starting values for the model parameters. Parameters for the primary model must be named in the usual way. Parameters for the secondary model are named as env_factor+'_'+parameter. For instance, the maximum growth temperature shall be named 'temperature_xmax'.

known_pars

A named vector of known model parameters (i.e. not fitted). They must be named using the same convention as for starting_point.

sec_model_names

A named character vector defining the secondary model for each environmental factor. The names define the factor and the value the type of model. Names must match columns in fit_data and env_conditions.

niter

number of iterations of the MCMC algorithm.

...

Additional arguments passed to modFit.

check

Whether to check model parameters (TRUE by default).

formula

an object of class "formula" describing the x and y variables. logN ~ time as a default.

logbase_mu

Base of the logarithm the growth rate is referred to. By default, the same as logbase_logN. See vignette about units for details.

logbase_logN

Base of the logarithm for the population size. By default, 10 (i.e. log10). See vignette about units for details.

Examples

# \donttest{
## We use the example data included in the package

data("example_dynamic_growth")
data("example_env_conditions")

## Definition of the secondary models
sec_model_names <- c(temperature = "CPM", aw= "CPM")

## Any model parameter can be fixed
known_pars <- list(Nmax = 1e4,  # Primary model
    N0 = 1e0, Q0 = 1e-3,  # Initial values of the primary model
    mu_opt = 4, # mu_opt of the gamma model
    temperature_n = 1,  # Secondary model for temperature
    aw_xmax = 1, aw_xmin = .9, aw_n = 1  # Secondary model for water activity
    )

## We need starting values for the remaining parameters

my_start <- list(temperature_xmin = 25, temperature_xopt = 35,
    temperature_xmax = 40,
    aw_xopt = .95)

## We can now call the fitting function

set.seed(12124) # Setting seed for repeatability

my_MCMC_fit <- fit_MCMC_growth(example_dynamic_growth, example_env_conditions,
    my_start, known_pars, sec_model_names, niter = 3000)

## Always check the MCMC chain!!

plot(my_MCMC_fit$fit_results)

## We can compare data against fitted curve

plot(my_MCMC_fit)

## Any environmental factor can be included using add_factor

plot(my_MCMC_fit, add_factor = "temperature",
    label_y1 = "Count (log CFU/ml)", label_y2 = "Temperature (C)")

# }