modelRegionalTempAR1SimpleYear: modelRegionalTempAR1SimpleYear

In Conte-Ecology/conteStreamTemperature: clean, prepare, and analyze stream temperature data

Description

modelRegionalTempAR1SimpleYear Linear mixed model in JAGS to model daily stream temperature

Usage

modelRegionalTempAR1SimpleYear(data = tempDataSyncS, cov.list,
  formulae = NULL, firstObsRows, evalRows, param.list, n.burn = 5000,
  n.it = 3000, n.thin = 3, nc = 3, coda = FALSE, runParallel = TRUE,
  cluster_type = NULL, data_dir = data_dir)

Arguments

data	dataframe created in the 3-statModelPrep.Rmd script
n.burn	Integer number of iterations in the burn-in (adapation) phase of the MCMC
n.it	Integer number of iterations per chain to run after the burn-in
n.thin	Integer: save every nth iteration
coda	Logical if TRUE return coda mcmc.list, if FALSE (default) return jags.samples object
data.fixed	Dataframe of fixed effects parameter names from columns in data. These are parameters without random slopes.
data.random.sites	Dataframe of variables to have random slopes by site
data.random.years	Dataframe of variables to have random slopes by year
deployments	Named vector of the logger deployment starting positions (rows) within the dataframe.
params	Character string of parameters to monitor (return) from the model
n.chains	Integer number of chains. One run per cluster so should use <= # cores on computer

Details

This function takes daily observed stream temperatures, air temperature, day of the year, and landscape covariates for a linear mixed effects model with site within HUC8 and year random effects.

Value

Returns the iterations from the Gibbs sampler for each variable in params as either an mcmc.list or jags.samples object

Examples

## Not run: 
M.ar <- modelRegionalTempAR(data, data.fixed, data.random.sites, data.random.years, n.burn = 1000, n.it = 1000, n.thin = 1, nc = 3, coda = coda.tf, param.list = monitor.params)

## End(Not run)

Conte-Ecology/conteStreamTemperature documentation built on Oct. 12, 2021, 10:26 p.m.