ungroup: Penalized Composite Link Model for Efficient Estimation of
Smooth Distributions from Coarsely Binned Data
Versatile method for ungrouping histograms (binned count data)
assuming that counts are Poisson distributed and that the underlying sequence
on a fine grid to be estimated is smooth. The method is based on the composite
link model and estimation is achieved by maximizing a penalized likelihood.
Smooth detailed sequences of counts and rates are so estimated from the binned
counts. Ungrouping binned data can be desirable for many reasons: Bins can be
too coarse to allow for accurate analysis; comparisons can be hindered when
different grouping approaches are used in different histograms; and the last
interval is often wide and open-ended and, thus, covers a lot of information
in the tail area. Age-at-death distributions grouped in age classes and
abridged life tables are examples of binned data. Because of modest assumptions,
the approach is suitable for many demographic and epidemiological applications.
For a detailed description of the method and applications see
Rizzi et al. (2015) <doi:10.1093/aje/kwv020>.
||R (≥ 3.4.0)
||pbapply (≥ 1.3), Rcpp (≥ 0.12.0), Rdpack (≥ 0.8), Matrix
||MortalityLaws (≥ 1.5.0), knitr (≥ 1.20), rmarkdown (≥
1.10), testthat (≥ 2.0.0)
||Marius D. Pascariu
Silvia Rizzi [aut],
Maciej J. Danko
||Marius D. Pascariu <rpascariu at outlook.com>
||MIT + file LICENSE
||ungroup citation info
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