Description Usage Arguments Details Value Author(s) References
A nearest-neighbors procedure is used in conjunction with the Epanechnikov kernel to define a kernel smooth of multinomial outcomes across the covariate space
1 | smooth.patterns(dat, kfrac, bw)
|
dat |
The capture-recapture data in the form that is returned by
|
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 |
See Kurtz 2013, Chapter on multiple sclerosis
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
Zach Kurtz
Kurtz 2013
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.