smooth.patterns: Local averaging for LLLMs

Description Usage Arguments Details Value Author(s) References

View source: R/regression.R

Description

A nearest-neighbors procedure is used in conjunction with the Epanechnikov kernel to define a kernel smooth of multinomial outcomes across the covariate space

Usage

1
smooth.patterns(dat, kfrac, bw)

Arguments

dat

The capture-recapture data in the form that is returned by formatdata or micro.post.stratify.

kfrac

The approximate fraction of the data that is included in the support of the kernel for the local averages.

bw

A matrix a single column, with rownames that match the covariate names in dat. The values in the column are scalars that are used in constructing distances between covariate vectors. Raw differences are divided by the corresponding scalars before being squared in the context of a Euclidean metric.

Details

See Kurtz 2013, Chapter on multiple sclerosis

Value

A list containing the original data (dat), the smoothed data (hpi), and the effective sample sizes (ess) for each local average, or row, in the smoothed data

Author(s)

Zach Kurtz

References

Kurtz 2013


lllcrc documentation built on May 30, 2017, 7:10 a.m.