BHfitting: BHfitting function

In femoerman/PBPGM: Parallellised Bayesian Population Growth Models

Description

Function to do the actual parallelised Beverton holt fitting of the models

Usage

BHfitting(
  BH.code,
  dd,
  K.prior,
  r0.prior,
  d.prior,
  N0.prior,
  sdev.prior,
  cores.to.use,
  iter,
  warmup,
  chains
)

Arguments

BH.code	Stan code generated by the GenerateBHcode 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
d.prior	Prior value for the mean death rate (d). 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

Examples

BHfitting()

femoerman/PBPGM documentation built on Aug. 22, 2021, 11:46 p.m.