summarize_SS

Provides functions to specify, fit and visualize
nested partially-latent class models (
Wu, Deloria-Knoll, Hammitt, and Zeger (2016) <doi:10.1111/rssc.12101>;
Wu, Deloria-Knoll, and Zeger (2017) <doi:10.1093/biostatistics/kxw037>;
Wu and Chen (2021) <doi:10.1002/sim.8804>) for
inference of population disease etiology and individual diagnosis. In the motivating
Pneumonia Etiology Research for Child Health (PERCH) study, because both quantities
of interest sum to one hundred percent, the PERCH scientists frequently refer to
them as population etiology pie and individual etiology pie, hence the name of the package.

Zhenke Wu

baker

"Nested Partially Latent Class Models"

Scott Zeger

John Muschelli

Irena Chen

summarize_SS function

<dl><dt>SS_dat</dt>
<dd>a data frame of silver-standard data. It can usually
be obtained by <code>data_nplcm$Mobs$MSS[[1]]</code>, meaning the first SS measurement
slice.</dd>
<dt>Y</dt>
<dd>a vector of case control status: 1 for case; 0 for control.</dd></dl>

Arguments

silver-standard data summary — summarize_SS

<dl>

<dt>SS_dat</dt>
<dd>a data frame of silver-standard data. It can usually
be obtained by <code>data_nplcm$Mobs$MSS[[1]]</code>, meaning the first SS measurement
slice.</dd>


<dt>Y</dt>
<dd>a vector of case control status: 1 for case; 0 for control.</dd>

</dl>

summarize_SS: silver-standard data summary

Description

Usage

Value

Arguments

See Also

Examples