dZIP

rZIP

Density and random generation for the zero inflated Poisson distribution.

A large number of measurements generate count data. This is a statistical data type
that only assumes non-negative integer values and is generated by counting. Typically, counting
data can be found in biomedical applications, such as the analysis of DNA double-strand breaks.
The number of DNA double-strand breaks can be counted in individual cells using various
bioanalytical methods. For diagnostic applications, it is relevant to record the distribution of
the number data in order to determine their biomedical significance (Roediger, S. et al., 2018.
Journal of Laboratory and Precision Medicine. <doi:10.21037/jlpm.2018.04.10>). The software
offers functions for a comprehensive automated evaluation of distribution models of count
data. In addition to programmatic interaction, a graphical user interface (web server)
is included, which enables fast and interactive data-scientific analyses. The user is supported
in selecting the most suitable counting distribution for his own data set.

Jaroslaw Chilimoniuk

countfitteR

Comprehensive Automatized Evaluation of Distribution Models for
Count Data

Alicja Gosiewska

Jadwiga Słowik

Michal Burdukiewicz

Stefan Roediger

zip function

<dl><dt>x</dt>
<dd>vector of (non-negative integer) quantiles.</dd>
<dt>lambda</dt>
<dd>vector of (non-negative) means.</dd>
<dt>r</dt>
<dd>probability of excess zeros.</dd>
<dt>n</dt>
<dd>number of random values to return.</dd></dl>

Arguments

Zero-inflated Poisson distrbution — zip

<dl>

<dt>x</dt>
<dd>vector of (non-negative integer) quantiles.</dd>


<dt>lambda</dt>
<dd>vector of (non-negative) means.</dd>


<dt>r</dt>
<dd>probability of excess zeros.</dd>


<dt>n</dt>
<dd>number of random values to return.</dd>

</dl>

zip: Zero-inflated Poisson distrbution

Description

Usage

Arguments

See Also

Examples