me_diag: Measurement error model diagnostics
In geostan: Bayesian Spatial Analysis

me_diag

R Documentation

Measurement error model diagnostics

Description

Visual diagnostics for spatial measurement error models.

Usage

me_diag(
  fit,
  varname,
  shape,
  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), quiet = TRUE),
  binwidth = function(x) 0.5 * sd(x)
)

Arguments

`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)`.

Value

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).

Source

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.

Examples


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(college = georgia$college.se),
                   car_parts = cars)
## sample from the prior probability model only, including the ME model
fit <- stan_glm(log(rate.male) ~ college,
                ME = ME,
                data = georgia, 
                prior_only = TRUE,
                iter = 1e3, # for speed only
                chains = 2, # for speed only
                refresh = 0 # silence some printing
                )

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

geostan documentation built on April 3, 2025, 10:04 p.m.