scanstatistics (version 1.0.1)

scan_eb_poisson: Calculate the expectation-based Poisson scan statistic.

Description

Calculate the expectation-based Poisson scan statistic devised by Neill et al. (2005).

Usage

scan_eb_poisson(counts, zones, baselines = NULL, population = NULL,
  n_mcsim = 0, max_only = FALSE)

Arguments

counts

Either:

• A matrix of observed counts. Rows indicate time and are ordered from least recent (row 1) to most recent (row nrow(counts)). Columns indicate locations, numbered from 1 and up. If counts is a matrix, the optional matrix argument baselines should also be specified.

• A data frame with columns "time", "location", "count", "baseline". Alternatively, the column "baseline" can be replaced by a column "population". The baselines are the expected values of the counts.

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 counts. Not needed if counts is a data frame. Holds the Poisson mean parameter for each observed count. Will be estimated if not supplied (requires the population argument). These parameters are typically estimated from past data using e.g. Poisson (GLM) regression.

population

Optional. A matrix or vector of populations for each location. Not needed if counts is a data frame. If counts is a matrix, population is only needed if baselines are to be estimated and you want to account for the different populations in each location (and time). If a matrix, should be of the same dimensions as counts. If a vector, should be of the same length as the number of columns in counts.

n_mcsim

A non-negative integer; the number of replicate scan statistics to generate in order to calculate a \(P\)-value.

max_only

Boolean. If FALSE (default) the log-likelihood ratio statistic for each zone and duration is returned. If TRUE, only the largest such statistic (i.e. the scan statistic) is returned, along with the corresponding zone and duration.

Value

A list which, in addition to the information about the type of scan statistic, has the following components:

MLC

A list containing the number of the zone of the most likely cluster (MLC), the locations in that zone, the duration of the MLC, the calculated score, and the relative risk. In order, the elements of this list are named zone_number, locations, duration, score, relative_risk.

observed

A data frame containing, for each combination of zone and duration investigated, the zone number, duration, score, relative risk. The table is sorted by score with the top-scoring location on top. If max_only = TRUE, only contains a single row corresponding to the MLC.

replicates

A data frame of the Monte Carlo replicates of the scan statistic (if any), and the corresponding zones and durations.

MC_pvalue

The Monte Carlo \(P\)-value.

Gumbel_pvalue

A \(P\)-value obtained by fitting a Gumbel distribution to the replicate scan statistics.

n_zones

The number of zones scanned.

n_locations

The number of locations.

max_duration

The maximum duration considered.

n_mcsim

The number of Monte Carlo replicates made.

References

Neill, D. B., Moore, A. W., Sabhnani, M. and Daniel, K. (2005). Detection of emerging space-time clusters. Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining - KDD <U+2019>05, 218.

Examples

# 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_eb_poisson(counts = counts,
                       zones = zones,
                       baselines = baselines,
                       n_mcsim = 99,
                       max_only = FALSE)
# }