me_diag function

Measurement error model diagnostics

Measurement error model diagnostics

Visual diagnostics for spatial measurement error models.

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.

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 ggplots 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).

Returns

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).

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)

See Also

sp_diag, moran_plot, mc, aple

  • Maintainer: Connor Donegan
  • License: GPL (>= 3)
  • Last published: 2024-12-04