View source: R/fit_multi_dynamic.R
fit_multiple_growth_MCMC  R Documentation 
The function fit_multiple_growth_MCMC()
has been superseded by the toplevel
function fit_growth()
, which provides a unified approach for growth modelling.
However, this functions can still be used to fit a growth model using a dataset comprised of several experiments with potentially different dynamic experimental conditions.
fit_multiple_growth_MCMC( starting_point, experiment_data, known_pars, sec_model_names, niter, ..., check = TRUE, formula = logN ~ time, logbase_mu = logbase_logN, logbase_logN = 10 )
starting_point 
a named vector of starting values for the model parameters. 
experiment_data 
a nested list with the experimental data. Each entry describes
one experiment as a list with two elements: data and conditions. 
known_pars 
named vector of known model parameters 
sec_model_names 
named character vector with names of the environmental conditions and values of the secondary model (see secondary_model_data). 
niter 
number of samples of the MCMC algorithm. 
... 
additional arguments for modMCMC (e.g. upper and lower bounds). 
check 
Whether to check the validity of the models. 
formula 
an object of class "formula" describing the x and y variables.

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. 
An instance of FitMultipleGrowthMCMC()
.
## We will use the multiple_experiments data set data("multiple_experiments") ## For each environmental factor, we need to defined a model sec_names < c(temperature = "CPM", pH = "CPM") ## Any model parameter can be fixed known < list(Nmax = 1e8, N0 = 1e0, Q0 = 1e3, temperature_n = 2, temperature_xmin = 20, temperature_xmax = 35, pH_n = 2, pH_xmin = 5.5, pH_xmax = 7.5, pH_xopt = 6.5) ## The rest require starting values for model fitting start < list(mu_opt = .8, temperature_xopt = 30) ## We can now call the fitting function set.seed(12412) global_MCMC < fit_multiple_growth_MCMC(start, multiple_experiments, known, sec_names, niter = 1000, lower = c(.2, 29), # lower limits of the model parameters upper = c(.8, 34)) # upper limits of the model parameters ## Parameter estimates can be retrieved with summary summary(global_MCMC) ## We can compare fitted model against observations plot(global_MCMC) ## Any single environmental factor can be added to the plot using add_factor plot(global_MCMC, add_factor = "temperature")
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