plot.miss: Plot samples from the output of MISS

Description Usage Arguments Details Author(s) See Also Examples

View source: R/plot.miss.R

Description

This may be used to plot, or save plots of, samples in an object of class miss. Plots include a trace plot, density plot, and autocorrelation or ACF plot.

Usage

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## S3 method for class 'miss'
plot(x, PDF=FALSE, ...)

Arguments

x

This required argument is an object of class miss.

PDF

This logical argument indicates whether or not the user wants Laplace's Demon to save the plots as a .pdf file.

...

Additional arguments are unused.

Details

The plots are arranged in a 3 x 3 matrix. Each row represents the predictive distribution of a missing value. The left column displays trace plots, the middle column displays kernel density plots, and the right column displays autocorrelation (ACF) plots.

Trace plots show the thinned history of the predictive distribution, with its value in the y-axis moving by iteration across the x-axis. Simulations of a predictive distribution with good properties do not suggest a trend upward or downward as it progresses across the x-axis (it should appear stationary), and it should mix well, meaning it should appear as though random samples are being taken each time from the same target distribution. Visual inspection of a trace plot cannot verify convergence, but apparent non-stationarity or poor mixing can certainly suggest non-convergence. A red, smoothed line also appears to aid visual inspection.

Kernel density plots depict the marginal posterior distribution. There is no distributional assumption about this density.

Autocorrelation plots show the autocorrelation or serial correlation between sampled values at nearby iterations. Samples with autocorrelation do not violate any assumption, but are inefficient because they reduce the effective sample size (ESS), and indicate that the chain is not mixing well, since each value is influenced by values that are previous and nearby. The x-axis indicates lags with respect to samples by iteration, and the y-axis represents autocorrelation. The ideal autocorrelation plot shows perfect correlation at zero lag, and quickly falls to zero autocorrelation for all other lags.

Author(s)

Statisticat, LLC [email protected]

See Also

MISS.

Examples

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### See the MISS function for an example.

LaplacesDemonR/LaplacesDemonCpp documentation built on May 7, 2019, 12:43 p.m.