A data set modified from the CrabSatellites data in countreg package (see demo(ODAH)).

datasets

A collection of privacy-preserving distributed algorithms for conducting multi-site data analyses. The regression analyses can be linear regression for continuous outcome, logistic regression for binary outcome, Cox proportional hazard regression for time-to event outcome, Poisson regression for count outcome, or multi-categorical regression for nominal or ordinal outcome. The PDA algorithm runs on a lead site and only requires summary statistics from collaborating sites, with one or few iterations. The package can be used together with the online system (<https://pda-ota.pdamethods.org/>) for safe and convenient collaboration. For more information, please visit our software websites: <https://github.com/Penncil/pda>, and <https://pdamethods.org/>.

Jiajie Chen

Privacy-Preserving Distributed Algorithms

Chongliang Luo

Rui Duan

Mackenzie Edmondson

Jiayi Tong

Xiaokang Liu

Kenneth Locke

Yong Chen

Penn Computing Inference Learning (PennCIL) lab 

cs function

A data frame containing 173 observations on 4 variables.<dl>
 <dt>site</dt>
<dd>Simulated site id, 85 'site1' and 88 'site2'.</dd> <dt>satellites</dt>
<dd>Number of satellites. Treated as (zero-inflated) count outcome in ODAH</dd> <dt>width</dt>
<dd>Carapace width (cm).</dd> <dt>weight</dt>
<dd>Weight (kg).</dd>
 </dl>

Format

CrabSatellites data — cs

A data frame containing 173 observations on 4 variables.<dl>
 <dt>site</dt>
<dd>Simulated site id, 85 'site1' and 88 'site2'.</dd>

 <dt>satellites</dt>
<dd>Number of satellites. Treated as (zero-inflated) count outcome in ODAH</dd>

 <dt>width</dt>
<dd>Carapace width (cm).</dd>

 <dt>weight</dt>
<dd>Weight (kg).</dd>
 

</dl>

cs: CrabSatellites data

Description

Usage

Arguments

Format