ofun

internal

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>.

Marius D. Pascariu

ungroup

Penalized Composite Link Model for Efficient Estimation of
Smooth Distributions from Coarsely Binned Data

Silvia Rizzi

Jonas Schoeley

Maciej J. Danko

ofun function

<dl><dt>L</dt>
<dd>lambda.hat</dd>
<dt>I</dt>
<dd>Input object from pclm function</dd>
<dt>type</dt>
<dd>Type of PCLM model. Options: <code>"1D", "2D"</code> for 
univariate and two-dimensional model respectively.</dd></dl>

Arguments

Objective function — ofun

<dl>

<dt>L</dt>
<dd>lambda.hat</dd>


<dt>I</dt>
<dd>Input object from pclm function</dd>


<dt>type</dt>
<dd>Type of PCLM model. Options: <code>"1D", "2D"</code> for 
univariate and two-dimensional model respectively.</dd>

</dl>

Objective function

ofun: Objective function

Description

Usage

Arguments