bridge_sampler(geostan_fit$stanfit)). By default, geostan
only collects MCMC samples for parameters that are expected to be of
some interest for users. To become compatible with bridgesampling, the
keep_all argument was added to all of the model fitting
functions. For important background and details see the bridgesampling
package documentation and vignettes on CRAN.lisa function would automatically
center and scale the variate before computing local Moran’s I. Now, the
variate will be centered and scaled by default but the user has the
option to turn the scaling off (so the variate will be centered, but not
divided by its standard deviation). This function also row-standardized
the spatial weights matrix automatically, but there was no reason why.
That’s not done anymore.The distance-based CAR models that are prepared by the
prep_car_data function have changed slightly. The
conditional variances were previously a function of the sum of
neighboring inverse distances (in keeping with the specification of the
connectivity matrix); this can lead to very skewed frequency
distributions of the conditional variances. Now, the conditional
variances are equal to the inverse of the number of neighboring sites.
This is in keeping with the more common CAR model specifications.
geostan now supports Poisson models with censored count data, a
common problem in public health research where small area disease and
mortality counts are censored below a threshold value. Model for
censored outcome data can now be implemented using the
censor_point argument found in all of the model fitting
functions (stan_glm, stan_car, stan_esf, stan_icar).
The measurement error models have been updated in three important respects:
?prep_me_data.?prep_me_data for usage.stan_car, ME models
automatically employed the CAR model as a prior for the modeled
covariates. That has changed, so that the default behavior for the ME
models is the same across all stan_* models (CAR, GLM, ESF,
ICAR).The second change listed above is particularly useful for variables
that are highly skewed, such as the poverty rate. To determine whether a
transformation should be considered, it can be helpful to evaluate
results of the ME model (with the untransformed covariate) using the
me_diag function. The logit transform is done on the
‘latent’ (modeled) variable, not the raw covariate. This transformation
cannot be applied to the raw data by the user because that would require
the standard errors of covariate estimates (e.g., ACS standard errors)
to be adjusted for the transformation.
A predict method has been introduced for fitted geostan
models; this is designed for calculating marginal effects. Fitted values
of the model are still returned using fitted and the
posterior predictive distribution is still accessible via
posterior_predict.
The centerx argument has been updated to handle
measurement error models for covariates. The centering now happens
inside the Stan model so that the means of the modeled covariates
(latent variables) are used instead of the raw data mean.
geostan’s first release.