Description Usage Arguments Examples
Function to do the actual parallelised r-K fitting of the models
1 2 3 4 5 6 7 8 9 10 11 12 | rKfitting(
rK.code,
dd,
K.prior,
r0.prior,
N0.prior,
sdev.prior,
cores.to.use,
iter,
warmup,
chains
)
|
rK.code |
Stan code generated by the GeneraterKcode functions |
dd |
Dataframe with columns for population size data (colname=popsize), time data (colname=time) and unique identifiers for each population (colname=ident) |
K.prior |
Prior value for the mean carrying capacity (K). Must be on log scale and numeric |
r0.prior |
Prior value for the mean intrinsic rate of growth (r0). Must be on log scale and numeric |
N0.prior |
Prior value for the mean starting population size (N0). Must be on log scale and numeric |
sdev.prior |
Prior value for the standard deviation for the model fitting. Must be numeric. Defaults to 1. |
cores.to.use |
Available cores to do the bayesian models |
iter |
Number of iterations to run for the model, defaults to 1e4. Must be integer |
warmup |
Number of iterations to run warmup. Defaults to 1e3. Must be integer and smaller than warmup |
chains |
Number of chains to run for each fit. Must be integer. Defaults to 1 |
Ksd.prior |
Prior value for the standard deviation on carrying capacity (K). Must be on log scale and numeric |
r0sd.prior |
Prior value for the standard deviation on intrinsic rate of growth (r0). Must be on log scale and numeric |
N0sd.prior |
Prior value for the standard deviation on starting population size (N0). Must be on log scale and numeric |
1 |
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