dssAggregate

The expectation and variance of aggregated predictions is just a sum if the
predictions are (conditionally) independent. This function computes the DSS
for a matrix of observations and a matrix of predictions
where the columns are to be summed according to a given factor.

Meyer and Held (2017) <doi:10.1093/biostatistics/kxw051> present an
age-structured spatio-temporal model for infectious disease counts. The
approach is illustrated in a case study on norovirus gastroenteritis in
Berlin, 2011-2015, by age group, city district and week, using additional
contact data from the POLYMOD survey. This package contains the data and
code to reproduce the results from the paper, see 'demo("hhh4contacts")'.

Sebastian Meyer

hhh4contacts

Age-Structured Spatio-Temporal Models for Infectious Disease
Counts

Leonhard Held

dssAggregate function

<dl><dt>observed</dt>
<dd>a numeric matrix of observed counts.</dd>
<dt>pred</dt>
<dd>a numeric matrix of predicted counts.</dd>
<dt>psi</dt>
<dd>a numeric vector or matrix of overdispersion parameters such that
<code>pred * (1 + pred/exp(psi))</code> is the prediction's variance.
Alternatively, <code>psi = NULL</code> indicated Poisson predictions.</dd>
<dt>groups</dt>
<dd>a factor variable of length <code>ncol(observed)</code> indicating
which columns should be aggregated.</dd></dl>

Arguments

Compute the DSS on Aggregated Predictions and Observations — dssAggregate

<dl>

<dt>observed</dt>
<dd>a numeric matrix of observed counts.</dd>


<dt>pred</dt>
<dd>a numeric matrix of predicted counts.</dd>


<dt>psi</dt>
<dd>a numeric vector or matrix of overdispersion parameters such that
<code>pred * (1 + pred/exp(psi))</code> is the prediction's variance.
Alternatively, <code>psi = NULL</code> indicated Poisson predictions.</dd>


<dt>groups</dt>
<dd>a factor variable of length <code>ncol(observed)</code> indicating
which columns should be aggregated.</dd>

</dl>

dssAggregate: Compute the DSS on Aggregated Predictions and Observations

Description

Usage

Value

Arguments