runMCMCCountry: Run MCMC estimation (single time series)

In MJAlexander/distortr: Temporal smoothing methods for demographic time series

Description

Run MCMC estimation of a single time series using JAGS.

Usage

runMCMCCountry(df, nyears, method, order = NULL, I = 2.5,
  matern.cov = TRUE, obs.err = FALSE, measurement.err = FALSE,
  nchains = 4, nburnin = 1000, niter = 1000 + 30000, nthin = 30,
  model.file.path = NULL)

Arguments

df	A dataframe of x and y observations, and standard errors around ys
nyears	number of years of observations
method	The method of smoothing to implement (choices: ar, arma, splines, gp)
order	The order of splines penalization (either 1 or 2)
I	Knot spacing for splines
matern.cov	Whether or not to use Matern covariance function. Default is TRUE.
obs.err	is TRUE if standard errors are observed
measurement.err	is TRUE if there is assumed to be measurement error
nchains	Number of MCMC chains
nburnin	Number of iterations to throw away as burn in.
niter	Number of total iterations.
nthin	Degree of thinning of MCMC chains
model.file.path	Text file which contains the model to be fitted. If NULL, the text file is drawn from the models folder.

Value

A JAGS model object

See Also

getResults, plotResults

Examples

nyears <- 100
prop.sample <- 0.7
obs.err <- TRUE
sigma.y <- 0.5
seed <- 123
method <- 'splines'
params <- list(sigma.alpha = 1, order = 1)
df <- simulateFluctuations(nyears, prop.sample, method, params, obs.err, sigma.y)
df$se <- 1
mod <- runMCMCCountry(df = df, nyears = 100, method = "splines", order = 1,
nchains = 4, nburnin = 100, niter = 100+3000, nthin = 3)

MJAlexander/distortr documentation built on July 17, 2020, 4:06 p.m.