Description Usage Arguments Value References Examples
Calculate the "Bayesian Spatial Scan Statistic" by Neill et al. (2006), adapted to a spatiotemporal setting. The scan statistic assumes that, given the relative risk, the data follows a Poisson distribution. The relative risk is in turn assigned a Gamma distribution prior, yielding a negative binomial marginal distribution for the counts under the null hypothesis. Under the alternative hypothesis, the
1 2 3 4  scan_bayes_negbin(counts, zones, baselines = NULL, population = NULL,
outbreak_prob = 0.05, alpha_null = 1, beta_null = 1,
alpha_alt = alpha_null, beta_alt = beta_null, inc_values = seq(1,
3, by = 0.1), inc_probs = 1)

counts 
Either:

zones 
A list of integer vectors. Each vector corresponds to a single zone; its elements are the numbers of the locations in that zone. 
baselines 
Optional. A matrix of the same dimensions as 
population 
Optional. A matrix or vector of populations for each
location. Not needed if 
outbreak_prob 
A scalar; the probability of an outbreak (at any time, any place). Defaults to 0.05. 
alpha_null 
A scalar; the shape parameter for the gamma distribution under the null hypothesis of no anomaly. Defaults to 1. 
beta_null 
A scalar; the scale parameter for the gamma distribution under the null hypothesis of no anomaly. Defaults to 1. 
alpha_alt 
A scalar; the shape parameter for the gamma distribution
under the alternative hypothesis of an anomaly. Defaults to the same value
as 
beta_alt 
A scalar; the scale parameter for the gamma distribution
under the alternative hypothesis of an anomaly. Defaults to the same value
as 
inc_values 
A vector of possible values for the increase in the mean (and variance) of an anomalous count. Defaults to evenly spaced values between 1 and 3, with a difference of 0.1 between consecutive values. 
inc_probs 
A vector of the prior probabilities of each value in

A list which, in addition to the information about the type of scan
statistic, has the following components: priors
(list),
posteriors
(list), MLC
(list) and marginal_data_prob
(scalar). The list MLC
has elements
The number of the spatial zone of the most likely cluster (MLC).
The most likely event duration.
The posterior log probability that an event is ongoing in the MLC.
The logarithm of the Bayes factor for the MLC.
The posterior probability that an event is ongoing in the MLC.
The locations involved in the MLC.
The list priors
has elements
The prior probability of no anomaly.
The prior probability of an anomaly.
A vectorof prior probabilities of each value in the
argument inc_values
.
The prior probability of an outbreak in any of the spacetime windows.
The list posteriors
has elements
The posterior probability of no anomaly.
The posterior probability of an anomaly.
A data frame with columns inc_values
and
inc_posterior
.
A data frame with columns zone
,
duration
, log_posterior
and
log_bayes_factor
, each row corresponding
to a spacetime window.
A matrix with the posterior anomaly probability of each locationtime combination.
A vector with the posterior probability of an anomaly at each location.
Neill, D. B., Moore, A. W., Cooper, G. F. (2006). A Bayesian Spatial Scan Statistic. Advances in Neural Information Processing Systems 18.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26  ## Not run:
set.seed(1)
# Create location coordinates, calculate nearest neighbors, and create zones
n_locs < 50
max_duration < 5
n_total < n_locs * max_duration
geo < matrix(rnorm(n_locs * 2), n_locs, 2)
knn_mat < coords_to_knn(geo, 15)
zones < knn_zones(knn_mat)
# Simulate data
baselines < matrix(rexp(n_total, 1/5), max_duration, n_locs)
counts < matrix(rpois(n_total, as.vector(baselines)), max_duration, n_locs)
# Inject outbreak/event/anomaly
ob_dur < 3
ob_cols < zones[[10]]
ob_rows < max_duration + 1  seq_len(ob_dur)
counts[ob_rows, ob_cols] < matrix(
rpois(ob_dur * length(ob_cols), 2 * baselines[ob_rows, ob_cols]),
length(ob_rows), length(ob_cols))
res < scan_bayes_negbin(counts = counts,
zones = zones,
baselines = baselines)
## End(Not run)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.