ts_postpred {jagshelper}

Time series plot of centered posterior predictive distribution

Description

Produces a plot of centered posterior predictive distributions associated with a vector of data (most likely a time series), defined as the difference between posterior predictive and posterior predictive median.

Also overlays the posterior predictive residuals, defined as the differences between data values and their respective posterior predictive medians.

While not an omnibus posterior predictive check, this plot can be useful for detecting an overparameterized model, or else improper specification of observation error.

It should be noted that this function will only produce meaningful results with a vector of data, as opposed to a single value.

The posterior predictive distribution can be specified in two possible ways: either a single output object from jagsUI with an associated parameter name, or as a matrix or data.frame of posterior samples.

Usage

ts_postpred(
  ypp,
  y,
  p = NULL,
  x = NULL,
  lines = FALSE,
  transform = c("none", "exp", "expit"),
  ...
)

Arguments

ypp

Either a matrix or data.frame of posterior samples, or an output object returned from jagsUI and a supplied parameter name

y

The associated data vector

p

A character name, if a jagsUI object is passed to ypp

x

The time measurements associated with time series y. If the default NULL is accepted, equally-spaced integer values will be used.

lines

Whether to add a line linking data time series points. Defaults to FALSE.

transform

Should the y-axis be (back)transformed? Options are "exp", indicating exponential, or "expit", indicating inverse-logit. Defaults to "none", indicating no transformation. Note: if transform="exp"is used, consider adding additional plotting argument log="y".

...

Additional arguments to envelope

Value

NULL

Note

This function assumes the existence of a matrix of posterior predictive samples corresponding to a data vector, the construction of which must be left to the user. This can be accomplished within JAGS, or using appropriate simulation from the posterior samples.

Author(s)

Matt Tyers

See Also

qq_postpred, check_Rhat, check_neff, traceworstRhat, plotRhats

Examples

# first, a quick look at the example data...
str(SS_data)
str(SS_out$sims.list$ypp)

# plotting the example posterior predictive distribution with the data
# points overlayed.  Note the overdispersion in the posterior predictive.
caterpillar(SS_out, p="ypp")
points(SS_data$y)

# using a jagsUI object as ypp input
ts_postpred(ypp=SS_out, p="ypp", y=SS_data$y)

# using a matrix as ypp input
ts_postpred(ypp=SS_out$sims.list$ypp, y=SS_data$y)

# exp transformation
ts_postpred(ypp=SS_out, p="ypp", y=SS_data$y, transform="exp")
ts_postpred(ypp=SS_out, p="ypp", y=SS_data$y, transform="exp", log="y")