Visual diagnostics for spatial measurement error models.

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: https://github.com/ConnorDonegan/survey-HBM.

- fit
A

`geostan_fit`

model object as returned from a call to one of the`geostan::stan_*`

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

- shape
An object of class

`sf`

or another spatial object coercible to`sf`

with`sf::st_as_sf`

.- probs
Lower and upper quantiles of the credible interval to plot.

- plot
If

`FALSE`

, return a list of`ggplot`

s and a`data.frame`

with the raw data values alongside a posterior summary of the modeled variable.- mc_style
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
Size of points and lines, passed to

`geom_pointrange`

.- index
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
Style of connectivity matrix; if

`w`

is not provided,`style`

is passed to`shape2mat`

and defaults to "W" for row-standardized.- w
An optional spatial connectivity matrix; if not provided, one will be created using

`shape2mat`

.- binwidth
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).

`sp_diag`

, `moran_plot`

, `mc`

, `aple`

```
# \donttest{
library(sf)
data(georgia)
## 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$ICE.se),
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)
# }
```