Fit a spatial regression model using eigenvector spatial filtering (ESF).
stan_esf(
formula,
slx,
re,
data,
C,
EV = make_EV(C, nsa = nsa, threshold = threshold),
nsa = FALSE,
threshold = 0.25,
family = gaussian(),
prior = NULL,
ME = NULL,
centerx = FALSE,
censor_point,
prior_only = FALSE,
chains = 4,
iter = 2000,
refresh = 500,
keep_all = FALSE,
slim = FALSE,
drop = NULL,
pars = NULL,
control = NULL,
quiet = FALSE,
...
)Chun, Y., D. A. Griffith, M. Lee and P. Sinha (2016). Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters. Journal of Geographical Systems, 18(1), 67-85. doi:10.1007/s10109-015-0225-3 .
Dray, S., P. Legendre & P. R. Peres-Neto (2006). Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecological Modeling, 196(3-4), 483-493.
Donegan, C., Y. Chun and A. E. Hughes (2020). Bayesian estimation of spatial filters with Moran’s Eigenvectors and hierarchical shrinkage priors. Spatial Statistics. doi:10.1016/j.spasta.2020.100450 (open access: doi:10.31219/osf.io/fah3z ).
Griffith, Daniel A., and P. R. Peres-Neto (2006). Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses. Ecology 87(10), 2603-2613.
Griffith, D., and Y. Chun (2014). Spatial autocorrelation and spatial filtering, Handbook of Regional Science. Fischer, MM and Nijkamp, P. eds.
Griffith, D., Chun, Y. and Li, B. (2019). Spatial Regression Analysis Using Eigenvector Spatial Filtering. Elsevier.
Piironen, J and A. Vehtari (2017). Sparsity information and regularization in the horseshoe and other shrinkage priors. In Electronic Journal of Statistics, 11(2):5018-5051.
A model formula, following the R formula syntax. Binomial models are specified by setting the left hand side of the equation to a data frame of successes and failures, as in cbind(successes, failures) ~ x.
Formula to specify any spatially-lagged covariates. As in, ~ x1 + x2 (the intercept term will be removed internally). When setting priors for beta, remember to include priors for any SLX terms.
To include a varying intercept (or "random effects") term, alpha_re, specify the grouping variable here using formula syntax, as in ~ ID. Then, alpha_re is a vector of parameters added to the linear predictor of the model, and:
alpha_re ~ N(0, alpha_tau)
alpha_tau ~ Student_t(d.f., location, scale).A data.frame or an object coercible to a data frame by as.data.frame containing the model data.
Spatial connectivity matrix. This will be used to calculate eigenvectors if EV is not provided by the user. See shape2mat. Use of row-normalization (as in `shape2mat(shape, 'W') is not recommended for creating EV. Matrix C will also be used ('as is') to create any user-specified slx terms.
A matrix of eigenvectors from any (transformed) connectivity matrix, presumably spatial or network-based (see make_EV). If EV is provided, still also provide a spatial weights matrix C for other purposes; threshold and nsa are ignored for user provided EV.
Include eigenvectors representing negative spatial autocorrelation? Defaults to nsa = FALSE. This is ignored if EV is provided.
Eigenvectors with standardized Moran coefficient values below this threshold value will be excluded from the candidate set of eigenvectors, EV. This defaults to threshold = 0.25, and is ignored if EV is provided.
The likelihood function for the outcome variable. Current options are family = gaussian(), student_t() and poisson(link = "log"), and binomial(link = "logit").
A named list of parameters for prior distributions (see priors):
The intercept is assigned a Gaussian prior distribution (see normal
Regression coefficients are assigned Gaussian prior distributions. Variables must follow their order of appearance in the model formula. Note that if you also use slx terms (spatially lagged covariates), and you use custom priors for beta, then you have to provide priors for the slx terms. Since slx terms are prepended to the design matrix, the prior for the slx term will be listed first.
For family = gaussian() and family = student_t() models, the scale parameter, sigma, is assigned a (half-) Student's t prior distribution. The half-Student's t prior for sigma is constrained to be positive.
nu is the degrees of freedom parameter in the Student's t likelihood (only used when family = student_t()). nu is assigned a gamma prior distribution. The default prior is prior = list(nu = gamma2(alpha = 3, beta = 0.2)).
The scale parameter for random effects, or varying intercepts, terms. This scale parameter, tau, is assigned a half-Student's t prior. To set this, use, e.g., prior = list(tau = student_t(df = 20, location = 0, scale = 20)).
The eigenvector coefficients are assigned the horseshoe prior (Piironen and Vehtari, 2017), parameterized by global_scale (to control overall prior sparsity), plus the degrees of freedom and scale of a Student's t model for any large coefficients (see priors). To allow the spatial filter to account for a greater amount of spatial autocorrelation (i.e., if you find the residuals contain spatial autocorrelation), increase the global scale parameter (to a maximum of global_scale = 1).
To model observational uncertainty (i.e. measurement or sampling error) in any or all of the covariates, provide a list of data as constructed by the prep_me_data function.
To center predictors on their mean values, use centerx = TRUE. If the ME argument is used, the modeled covariate (i.e., latent variable), rather than the raw observations, will be centered. When using the ME argument, this is the recommended method for centering the covariates.
Integer value indicating the maximum censored value; this argument is for modeling censored (suppressed) outcome data, typically disease case counts or deaths. For example, the US Centers for Disease Control and Prevention censors (does not report) death counts that are nine or fewer, so if you're using CDC WONDER mortality data you could provide censor_point = 9.
Draw samples from the prior distributions of parameters only.
Number of MCMC chains to estimate. Default chains = 4.
Number of samples per chain. Default iter = 2000.
Stan will print the progress of the sampler every refresh number of samples. Defaults to 500; set refresh=0 to silence this.
If keep_all = TRUE then samples for all parameters in the Stan model will be kept; this is necessary if you want to do model comparison with Bayes factors and the bridgesampling package.
If slim = TRUE, then the Stan model will not collect the most memory-intensive parameters (including n-length vectors of fitted values, log-likelihoods, and ME-modeled covariate values). This will disable many convenience functions that are otherwise available for fitted geostan models, such as the extraction of residuals, fitted values, and spatial trends, WAIC, and spatial diagnostics, and ME diagnostics; many quantities of interest, such as fitted values and spatial trends, can still be calculated manually using given parameter estimates. The "slim" option is useful for data-intensive routines, such as regression with raster data, Monte Carlo studies, and measurement error models. For more control over which parameters are kept or dropped, use the drop argument instead of slim.
Provide a vector of character strings to specify the names of any parameters that you do not want MCMC samples for. Dropping parameters in this way can improve sampling speed and reduce memory usage. The following parameter vectors can potentially be dropped from ESF models:
The N-length vector of fitted values
Vector of 'random effects'/varying intercepts.
N-length vector of 'latent'/modeled covariate values created for measurement error (ME) models.
The N-length eigenvector spatial filter.
The vector of coefficients for the eigenvectors.
If slim = TRUE, then drop will be ignored.
Optional; specify any additional parameters you'd like stored from the Stan model.
A named list of parameters to control the sampler's behavior. See stan for details.
By default, any prior distributions that have not been assigned by the user are printed to the console. If quiet = TRUE, these will not be printed.
Other arguments passed to sampling.
An object of class class geostan_fit (a list) containing:
Summaries of the main parameters of interest; a data frame
Residual spatial autocorrelation as measured by the Moran coefficient.
a data frame containing the model data
A matrix of eigenvectors created with w and geostan::make_EV
The spatial weights matrix used to construct EV
the user-provided or default family argument used to fit the model
The model formula provided by the user (not including ESF component)
The slx formula
A list containing re, the random effects (varying intercepts) formula if provided, and
data a data frame with columns id, the grouping variable, and idx, the index values assigned to each group.
Prior specifications.
If covariates are centered internally (centerx = TRUE), then x_center is a numeric vector of the values on which covariates were centered.
The ME data list, if one was provided by the user for measurement error models.
A data frame with the name of the spatial component parameter ("esf") and method ("ESF")
an object of class stanfit returned by rstan::stan
Eigenvector spatial filtering (ESF) is a method for spatial regression analysis. ESF is extensively covered in Griffith et al. (2019). This function implements the methodology introduced in Donegan et al. (2020), which uses Piironen and Vehtari's (2017) regularized horseshoe prior.
ESF decomposes spatial autocorrelation into a linear combination of various patterns, typically at different scales (such as local, regional, and global trends). By adding a spatial filter to a regression model, these spatial autocorrelation patterns are shifted from the residuals to the spatial filter. ESF models take the spectral decomposition of a transformed spatial connectivity matrix, \(C\). The resulting eigenvectors, \(E\), are mutually orthogonal and uncorrelated map patterns. The spatial filter equals \(E \beta_{E}\) where \(\beta_{E}\) is a vector of coefficients.
ESF decomposes the data into a global mean, \(\alpha\), global patterns contributed by covariates \(X \beta\), spatial trends \(E \beta_{E}\), and residual variation. Thus, for family=gaussian(),
$$ y \sim Gauss(\alpha + X * \beta + E \beta_{E}, \sigma). $$
An ESF component can be incorporated into the linear predictor of any generalized linear model. For example, a spatial Poisson model for rare disease incidence may be specified as follows:
$$y \sim Poisson(e^{O + \mu})$$ $$\mu = \alpha + E \beta_{E} + A $$ $$ A \sim Guass(0, \tau) $$ $$ \tau \sim student(20, 0, 2) $$ $$ \beta_{E} \sim horseshoe(.) $$
The form of this model is similar to the BYM model (see stan_icar), in the sense that it contains a spatially structured trend term (\(E \beta_{E}\)) and an unstructured ('random effects') term (\(A\)).
The spatial.geostan_fit method will return \(E \beta_{E}\).
The model can also be extended to the space-time domain; see shape2mat to specify a space-time connectivity matrix.
The coefficients \(\beta_{E}\) are assigned the regularized horseshoe prior (Piironen and Vehtari, 2017), resulting in a relatively sparse model specification. In addition, numerous eigenvectors are automatically dropped because they represent trace amounts of spatial autocorrelation (this is controlled by the threshold argument). By default, stan_esf will drop all eigenvectors representing negative spatial autocorrelation patterns. You can change this behavior using the nsa argument.
The CAR models can also incorporate spatially-lagged covariates, measurement/sampling error in covariates (particularly when using small area survey estimates as covariates), missing outcome data, and censored outcomes (such as arise when a disease surveillance system suppresses data for privacy reasons). For details on these options, please see the Details section in the documentation for stan_glm.
# \donttest{
data(sentencing)
# spatial weights matrix with binary coding scheme
C <- shape2mat(sentencing, style = "B", quiet = TRUE)
# expected number of sentences
log_e <- log(sentencing$expected_sents)
# fit spatial Poisson model with ESF + unstructured 'random effects'
fit.esf <- stan_esf(sents ~ offset(log_e),
re = ~ name,
family = poisson(),
data = sentencing,
C = C,
chains = 2, iter = 800) # for speed only
# spatial diagnostics
sp_diag(fit.esf, sentencing)
# plot marginal posterior distributions of beta_ev (eigenvector coefficients)
plot(fit.esf, pars = "beta_ev")
# calculate log-standardized incidence ratios (SIR)
# # SIR = observed/exected number of cases
# in this case, prison sentences
library(ggplot2)
library(sf)
f <- fitted(fit.esf, rates = FALSE)$mean
SSR <- f / sentencing$expected_sents
log.SSR <- log( SSR, base = 2 )
# map the log-SSRs
ggplot(sentencing) +
geom_sf(aes(fill = log.SSR)) +
scale_fill_gradient2(
midpoint = 0,
name = NULL,
breaks = seq(-3, 3, by = 0.5)
) +
labs(title = "Log-Standardized Sentencing Ratios",
subtitle = "log( Fitted/Expected ), base 2"
) +
theme_void()
# }