A suite of graphical and tabular displays for model checking.
File
Description
op/
Observed vs. predicted plots
ppc/
Graphical displays comparing the observed data to data simulated from the posterior predictive distribution. For mixed models, there will be files for each of the distributions. If the model fits well, we should be able to use it to generate data that looks a lot like the data we have in-hand. Subfolders for outputs by stratum and unit or other grouping variables specified by ppc facets in the analysis config file (see HERE for more).
A scatterplot of observed (
\(y\)
-axis) vs. the median predicted (
\(x\)
) values. The horizontal lines / whiskers around each point correspond to the 95% HDI for the \(y^{rep}\)
’s associated with each observation.
Graphical displays comparing the observed data to data simulated from the posterior predictive distribution. For mixed models, there will be files for each of the distributions. If the model fits well, we should be able to use it to generate data that looks a lot like the data we have in-hand.
Subfolders for outputs by stratum and unit and any other faceting variables specified by ppc facets, as described here.
File
Description
y-rep-bayes-p-by-stratum_id[unit_code].csv
Bayesian p values for mu and sigma by stratum or unit code
y-rep-by-stratum_id[unit_code]-9-draws.jpg
Separate histograms of \(y\)
and some (
\(n=9\)
) of the \(y^{rep}\)
datasets
y-rep- by-stratum_id[unit_code]-stats.jpg
The distribution of test statistics. The solid, vertical line is the value of the test statistic computed from the observed data, \(y\)
, while the underlying bars represents the distribution of the test statistic in the \(y^{rep}\)
simulations.
Residuals for each observation. Used as input to variograms.
variograms.png
Figure shows degree of spatial autocorrelation in data, semi-variance by distance by direction or by any direction. Note that the default is to evaluate up to 1/3 of the total distance represented in the data.