Spatially weighted context data for multilevel modelling
Description
The package provides tools to construct and exploit
spatially weighted context data. Spatial weights are derived by
a Kernel function from a user-defined matrix of distances
between contextual units. Spatial weights can then be applied
either to precise contextual measures or to aggregate estimates
based on micro-level survey data, to compute spatially weighted
context data. Available aggregation functions include
indicators of central tendency, dispersion, or inter-group
variability, and take into account survey design weights. The
package further allows combining the resulting spatially
weighted context data with individual-level predictor and
outcome variables, for the purposes of multilevel modelling. An
ad hoc stratified bootstrap resampling procedure generates
robust point estimates for multilevel regression coefficients
and model fit indicators, and computes confidence intervals
adjusted for measurement dependency and measurement error of
aggregate estimates. As an additional feature, residual and
explained spatial dependency can be estimated for the tested
models.