sim_SA_true

True values of the parameters used to generate the simulated data set <a href="/link/sim_SA?package=sourceR&version=0.1.0" rd-options="" data-mini-rdoc="sourceR::sim_SA">sim_SA</a>.

datasets

Implements a non-parametric source attribution model
    to attribute cases of disease to sources in Bayesian framework with source and
    type effects. Type effects are clustered using a Dirichlet Process. Multiple
    times and locations are supported.

Poppy Miller

sourceR

Fits a Non-Parametric Bayesian Source Attribution Model

sim_SA_true function

The format is:
<tabular>lll</tabular>{
  <em>Parameter</em> 	<em>Format</em>a 	Dataframe with vectors giving the value, source, time and location identifiers for each source effect. q 	Dataframe with vectors giving the value and type identifiers for each source effect. alpha 	Gives the concentration parameter for the Dirichlet Process. cluster 	Dataframe with vectors giving the value and type identifiers for each source effect. lambda_i 	Dataframe with vectors giving the value, type, time and location identifiers for each lambda i (number of cases attributed to each type). lambda_j 	Dataframe with vectors giving the value, source, time and location identifiers for each lambda j (number of cases attributed to each source). }

sim_SA_true: True values of the parameters used to generate the simulated data set sim_SA.

Description

Usage

Arguments

format

Examples