density.mcmcSTmodel: Kernel Density Estimation for an 'mcmcSTmodel' Object

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/MCMC.R

Description

density method for class mcmcSTmodel.

Usage

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## S3 method for class 'mcmcSTmodel'
density(x, BurnIn = 0, estSTmodel = NULL, ...)

Arguments

x

mcmcSTmodel object

BurnIn

Number of initial points to ignore.

estSTmodel

Either a estimateSTmodel object from estimate.STmodel or a matrix with parameter-estimates and standard deviations, such as the output from coef.estimateSTmodel. If given as a matrix, it should have columns named "par" and "sd", and rows named after the parameters.

...

Additional parameters passed to density.

Details

Computes kernel density estimates for the MCMC-parameters; as well as approximate Gaussian densities based on the Fischer-information.

Value

List containing density estimate and Gaussian densities for all model parameters.

Author(s)

Johan Lindstrom

See Also

Other mcmcSTmodel methods: MCMC.STmodel, plot.density.mcmcSTmodel, plot.mcmcSTmodel, print.mcmcSTmodel, print.summary.mcmcSTmodel, summary.mcmcSTmodel

Examples

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##load estimation results
data(est.mesa.model)
##and MCMC results instead
data(MCMC.mesa.model)

##compute density estimates for the results, and use the Gaussian approximation
##based on Fischer information as reference.
dens <- density(MCMC.mesa.model, estSTmodel=est.mesa.model)

##all the estimated densities
str(dens,1)

##or results for one paramter
dens[[1]]

##plot density functions
plot(dens)
##for a different paramter, along with Gaussian approx
plot(dens, 3, norm.col="red")

##all covariance parameters
par(mfrow=c(3,3),mar=c(4,4,2.5,.5))
for(i in 9:17){
  plot(dens, i, norm.col="red")
}

SpatioTemporal documentation built on June 25, 2018, 9:03 a.m.