runstan: runstan

In kralljr/stansa: Bayesian Source Apportionment Using RStan

Description

runstan Simulate all data

Usage

runstan(
  N,
  typesim,
  stancode = NULL,
  stantype = NULL,
  prof = prof,
  meansd = meansd,
  keep = "all",
  rmout = F,
  sderr = NULL,
  seeds = NULL,
  iter = 1000,
  log1 = T,
  chains = 1,
  findamb = NULL,
  notes = NULL,
  fp = NULL,
  names = F,
  ...
)

Arguments

N	Number of days to simulate. If not noninformative stan, vector of 2 for days in ambient, days in local
typesim	Which simulation to run. Currently, local1 - local4, ambient.
stancode	Type of stan model to run
stantype	Type of stan model (can differ from code: for ifelse statements)
prof	Profile data, defaults to prof.
meansd	Matrix with columns corresponding to source, type, mean, sd, defaults to meansd.
keep	What to keep
rmout	Whether to remove largest (outliers)
sderr	Standard deviation of error. If not noninformative stan, can be vector of 2 for ambient local
iter	Number of iterations to run
log1	Whether to use lognormal for G
chains	Number of chains
findamb	Results from find ambient for informative
seed	Seeds for stan data simulation and running (length 2). If inform, seed must be length 3

kralljr/stansa documentation built on Dec. 21, 2021, 7:44 a.m.