Visual diagnostics for spatial measurement error models.

  probs = c(0.025, 0.975),
  plot = TRUE,
  mc_style = c("scatter", "hist"),
  size = 0.25,
  index = 0,
  style = c("W", "B"),
  w = shape2mat(shape, match.arg(style)),
  binwidth = function(x) 0.5 * sd(x)


Donegan, Connor and Chun, Yongwan and Griffith, Daniel A. (2021). ``Modeling community health with areal data: Bayesian inference with survey standard errors and spatial structure.'' Int. J. Env. Res. and Public Health 18 (13): 6856. DOI: 10.3390/ijerph18136856 Data and code:



A geostan_fit model object as returned from a call to one of the geostan::stan_* functions.


Name of the modeled variable (a character string, as it appears in the model formula).


An object of class sf or another spatial object coercible to sf with sf::st_as_sf.


Lower and upper quantiles of the credible interval to plot.


If FALSE, return a list of ggplots and a data.frame with the raw data values alongside a posterior summary of the modeled variable.


Character string indicating how to plot the Moran coefficient for the delta values: if mc = "scatter", then moran_plot will be used with the marginal residuals; if mc = "hist", then a histogram of Moran coefficient values will be returned, where each plotted value represents the degree of residual autocorrelation in a draw from the join posterior distribution of delta values.


Size of points and lines, passed to geom_pointrange.


Integer value; use this if you wish to identify observations with the largest n=index absolute Delta values; data on the top n=index observations ordered by absolute Delta value will be printed to the console and the plots will be labeled with the indices of the identified observations.


Style of connectivity matrix; if w is not provided, style is passed to shape2mat and defaults to "W" for row-standardized.


An optional spatial connectivity matrix; if not provided, one will be created using shape2mat.


A function with a single argument that will be passed to the binwidth argument in geom_histogram. The default is to set the width of bins to 0.5 * sd(x).


A grid of spatial diagnostic plots for measurement error models comparing the raw observations to the posterior distribution of the true values. Includes a point-interval plot of raw values and modeled values; a Moran scatter plot for delta = z - x where z are the survey estimates and x are the modeled values; and a map of the delta values (take at their posterior means).

See also


# \donttest{
## binary adjacency matrix
A <- shape2mat(georgia, "B")
## prepare data for the CAR model, using WCAR specification
cars <- prep_car_data(A, style = "WCAR")
## provide list of data for the measurement error model
ME <- prep_me_data(se = data.frame(ICE = georgia$,
                   car_parts = cars)
## sample from the prior probability model only, including the ME model
fit <- stan_glm(log(rate.male) ~ ICE,
                ME = ME,
                data = georgia, 
                prior_only = TRUE,
                iter = 800, # for speed only
                chains = 2, # for speed only
                refresh = 0 # silence some printing

## see ME diagnostics
me_diag(fit, "ICE", georgia)
## see index values for the largest (absolute) delta values
 ## (differences between raw estimate and the posterior mean)
me_diag(fit, "ICE", georgia, index = 3)
# }