MUPPET Model Checking Plotting Functions

Internal functions for generating posterior predictive model checking plots.

Usage

model.checking.plot(
  var.name,
  summary.simulate.data,
  summary.input.data,
  bins = 30
)

model.checking.cor.plot(cor.simulate.long, cor.input.long, bins = 30)

model.checking.res.cor.plot(res.cor.long)

model.checking.fit.plot(fit.long, fit.name)

ppp.plot(ppp_data, cor_type, cor_map, var_order)

Arguments

var.name

Name of the statistic to be plotted.

summary.simulate.data

Data frame containing univariate statistics calculated from posterior predicted data.

summary.input.data

Data frame containing univariate statistics calculated from the observed data.

bins

Number of bins in the histogram.

cor.simulate.long

Data frame containing pairwise correlations calculated from posterior predicted data.

cor.input.long

Data frame containing pairwise correlations calculated from the observed data.

res.cor.long

Data frame containing pairwise residual correlations calculated from posterior predicted data and the observed data.

fit.long

Data frame containing fit indices calculated from posterior predicted data and the observed data.

fit.name

Name of the fit index to be plotted.

ppp_data

Data frame containing posterior predictive p-values.

cor_type

Type of correlations to be plotted (pairwise correlations or pairwise residual correlations).

cor_map

Data frame mapping correlation statistic names to the names of the corresponding variable pairs.

var_order

Order of variables in the observed data.

Value

A ggplot object.

Functions

• model.checking.plot(): Generate a histogram of posterior predictive values for a univariate statistic, with the realized value indicated by a dashed vertical line.

• model.checking.cor.plot(): Generate histograms of posterior predictive values for all pairwise correlations in a single faceted plot, with the realized values indicated by dashed vertical lines.

• model.checking.res.cor.plot(): Generate scatter plots of posterior predictive residual correlations against realized residual correlations for all variable pairs in a single faceted plot.

• model.checking.fit.plot(): Generate a scatter plot of posterior predictive values against realized values for a fit index.

• ppp.plot(): Generate a heat map of posterior predictive p-values for pairwise correlations or pairwise residual correlations.