sim_SA: Simulated data: Human cases of campylobacteriosis and numbers...
In sourceR: Fits a Non-Parametric Bayesian Source Attribution Model

Description Usage Format

A simulated dataset containing the number of human cases of campylobacteriosis, the numbers of source samples positive for Campylobacter for each bacterial subtype, and the overall source prevalence.

sim_SA

A list containing the human cases (‘cases’), source samples (‘sources’), prevalences (‘prev’) and true values (‘truevals’).

cases: data frame with 364 rows and 4 variables:

Human: number of human cases of campylobacteriosis
Time: Time id for the samples
Location: Location id for the samples
Type: MLST type id for the samples

sources: data frame with 1092 rows and 4 variables

Count: number of source samples positive for campylobacteriosis
Time: Time id for the samples
Source: Source id for the samples
Type: MLST type id for the samples

prev: data frame with 12 rows and 3 variables

Value: Prevalence value (number of positive samples divided by total number of samples)
Time: Time id for the samples
Source: Source id for the samples

truevals: list containing a long format data frame for each model parameter giving the true value of that parameter.

alpha: A dataframe with 24 rows and 4 variables: Value contains the true alpha values, Time, Location and Source contain the time, location and source id's respectively.
q: A dataframe with 91 rows and 2 variables: Value contains the true q values, and Type contains the type id's.
lambda_i: A dataframe with 364 rows and 4 variables: Value contains the true lambda_i values, Time, Location and Type contain the time, location and type id's respectively.
xi: A dataframe with 24 rows and 4 variables: Value contains the true xi values, Time, Location and Source contain the time, location and source id's respectively.
r: A dataframe with 2184 rows and 5 variables: Value contains the true r values, Time, Type, Location and Source contain the time, type, location and source id's respectively.