Returns the posterior summary of the global (non-spatial) regression terms:
estimate, error and credible interval per predictor, as
brms::fixef() reports them. Reach for it to read the average effect
of a predictor with its uncertainty attached (the Bayesian counterpart to
a coefficient table), remembering that the Gaussian-process term has
already absorbed the spatially structured part of the signal, so these are
effects net of location.
Usage
# S3 method for class 'bayesian_fit'
coef(object, ...)Value
A matrix of fixed-effect posterior summaries, as returned by
brms::fixef(), on the fitted scale (per standard deviation of each
predictor under standardize_predictors = TRUE; see above). Never
NULL: a missing 'brms' or a failing
fixef() call errors, following the coef() contract described
in new_spatial_fit.
Standardised predictors
The summaries are on the scale the model was fitted on. A fit made with
standardize_predictors = TRUE was fitted on centred and scaled
numeric predictors, so each slope is the change in the linear predictor per
standard deviation of its predictor and the intercept is its value
at the predictor means, not the raw-unit numbers stats::lm()
reports on the same formula. Nothing on the returned matrix says so;
print() on the fit does, and the centre and scale of each predictor
are in object$info$predictor_scaling. To put a slope back in raw
units divide its Estimate, Est.Error and interval bounds by
that predictor's scale. The intercept's Estimate follows by
linearity (subtract each raw-unit slope times its predictor's
center), but its Est.Error and interval depend on the
posterior covariance of the coefficients: transform the draws from
brms::as_draws_df(object$engine) for those, or refit without
standardising.
See also
Other methods on a fitted model:
coef.gwr_fit(),
coef.rf_fit(),
fitted.bayesian_fit(),
fitted.gwr_fit(),
fitted.rf_fit(),
model_metrics(),
predict.bayesian_fit(),
predict.gwr_fit(),
predict.rf_fit(),
print.rf_fit(),
print.spatial_fit(),
residuals.bayesian_fit(),
residuals.gwr_fit(),
residuals.rf_fit(),
summary.spatial_fit()