cudia: CUDIA: cross-level imputation framework

In cudia: CUDIA Cross-level Imputation

Description

Estimate the CUDIA model parameters, then output cross-level imputed values. The default algorithm is set to the Bregman deterministic clustering algorithm in the referenced paper. Currently, only Gaussian-type data are supported.

Usage

cudia(formula, data, K, ...)

Arguments

formula	a symbolic description of the model to be fit. e.g. x~y+z means that the aggregate-level summary x is cross-level imputed using individual-level data y and z.
data	a data frame object in the model.
K	a number of intrinsic clusters.
...	other algorithm operational parameters

Value

An object of class cudia, basically a list including elements

indiv	original individual-level data
fitted.values	cross-level imputed aggregated data
theta	parameter vectors for individual-level clusters
eta	a parameter vector for aggregate-level clusters
Nk	estimated cluster sizes
xlab	variable names of individual-level data

Author(s)

Yubin Park

References

Y. Park and J. Ghosh, CUDIA: Probabilistic Cross-level Imputation using Individual Auxiliary Information, ACM Trans-IST, 2012.

See Also

print, plot methods

Examples

data(cudia_simul,package="cudia")
mod.sim <- cudia(aggr~indiv,cudia_simul,K=3)
summary(mod.sim)

cudia documentation built on May 29, 2017, 10:29 p.m.