run_mod: Fit errors-in-variables models with JAGS

In ncahill89/EIVmodels: Fit Errors-in-variables regression model

Fit errors-in-variables models with JAGS

Description

Fit errors-in-variables models with JAGS

Usage

run_mod(
  dat,
  model = "slr",
  EIV = TRUE,
  BP_scale = FALSE,
  n_cp = 1,
  igp_smooth = 0.2,
  iter = 5000,
  burnin = 1000,
  thin = 4,
  scale_factor_x = 1,
  scale_factor_y = 1
)

Arguments

dat	Input data with columns x,x_err,y,y_err
model	The model to run. Choose from slr, cp, gp, igp
EIV	Use EIV framework. Defaults to TRUE
BP_scale	Present the data as Before Present (BP). Defaults to FALSE.
n_cp	Number of change points if model = "cp" is chosen. Can choose from 1,2,3,4.
igp_smooth	Informs prior for the smoothness (correlation) parameter if model = "igp" is chosen. Choose a value between 0 and 1. Closer to 1 will increase smoothness.
iter	MCMC iterations
burnin	MCMC burnin
thin	MCMC thinning
scale_factor_x	value to divide the predictor (x) by to change the scale. 1 will result in no change. 1000 is recommended if x is years.
scale_factor_y	value to divide the response (y) by to change the scale. 1 will result in no change.

Value

List with data, JAGS input data, model output and the name of the model file.

Examples

dat <- sim_slr(n_sim = 30)
mod <- run_mod(dat, model = "slr")

ncahill89/EIVmodels documentation built on Dec. 5, 2022, 2:10 p.m.