View source: R/scan_eb_poisson.R
scan_eb_poisson | R Documentation |
Calculate the expectation-based Poisson scan statistic devised by Neill et al. (2005).
scan_eb_poisson( counts, zones, baselines = NULL, population = NULL, n_mcsim = 0, gumbel = FALSE, max_only = FALSE )
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 |
n_mcsim |
A non-negative integer; the number of replicate scan statistics to generate in order to calculate a P-value. |
gumbel |
Logical: should a Gumbel P-value be calculated? Default is
|
max_only |
Boolean. If |
A list which, in addition to the information about the type of scan statistic, has the following components:
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
.
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.
A data frame of the Monte Carlo replicates of the scan statistic (if any), and the corresponding zones and durations.
The Monte Carlo P-value.
A P-value obtained by fitting a Gumbel distribution to the replicate scan statistics.
The number of zones scanned.
The number of locations.
The maximum duration considered.
The number of Monte Carlo replicates made.
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 ’05, 218.
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)
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