sim_SA_true


True values of the parameters used to generate the simulated data set <a rd-options="" href="/link/sim_SA?package=sourceR&version=0.2.0" 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:
<table><tr><td>
  <em>Parameter</em> </td>
<td> <em>Format</em></td>
</tr><tr><td>
  a </td>
<td> Dataframe with vectors giving the value, source, time and location identifiers for each </td>
</tr><tr><td>
  </td>
<td> source effect. </td>
</tr><tr><td>
  q </td>
<td> Dataframe with vectors giving the value and type identifiers for each source effect. </td>
</tr><tr><td>
  alpha </td>
<td> Gives the concentration parameter for the Dirichlet Process. </td>
</tr><tr><td>
  cluster </td>
<td> Dataframe with vectors giving the value and type identifiers for each source effect. </td>
</tr><tr><td>
  lambda_i </td>
<td> Dataframe with vectors giving the value, type, time and location identifiers for each </td>
</tr><tr><td>
  </td>
<td> lambda i (number of cases attributed to each type). </td>
</tr><tr><td>
  lambda_j </td>
<td> Dataframe with vectors giving the value, source, time and location identifiers for </td>
</tr><tr><td>
  </td>
<td> each lambda j (number of cases attributed to each source). </td>
</tr></table>

Format


True values of the parameters used to generate the simulated data set <a rd-options='' href='sim_SA'>sim_SA</a>.


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

Description

Usage

Arguments

Format

Examples

Parameter	Format
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).