Plot standard McMC convergence diagnostics to help determine lack of model convergence.

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Description

Takes a fitted fit_ssm object and uses standard McMC convergence diagnostic plots to aid assessment of lack of convergence.

Usage

1
diag_ssm(fit)

Arguments

fit

an output object from fit_ssm

Value

Uses plotting functions from Martyn Plummer's coda package to help diagnose lack of convergence for the core model parameters. The traceplot shows the time series for both McMC chains; the density plot shows the density estimate for each parameter; the autocorrelation plots show the within-chain sample autocorrelation for each parameter; the G-B-R shrink factor plot shows the evolution of Gelman and Rubin's shrink factor for increasing number of iterations. See the coda package for further details.

References

Brooks SP, Gelman A (1998) General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7:434-455

Examples

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## Not run: 
data(ellie)
fit.s <- fit_ssm(ellie, model = "DCRWS", tstep = 1, adapt = 2000, samples = 1000, 
              thin = 2, span = 0.1)
diag_ssm(fit.s)

# increase burnin, posterior sample numbers, and thinning factor
fit.s2 <- fit_ssm(ellie, model = "DCRWS", tstep = 1, adapt = 5000, samples = 5000, 
              thin = 5, span = 0.1)
diag_ssm(fit.s2)

## End(Not run)