For each cluster, this function determines a ring of equal number of observations and/or equal radius and calculates several indicators from observations located inside that ring.
object of class prevR.
minimum number of observations.
maximum rings radius (in kilometers if coordinates in decimal degrees, in the unit of the projection otherwise).
show a progress bar?
For each ligne of the data frame
a ring, centred on the cluster. It could be:
rings of eaqul number of observations if
N is finite and
rings of equal radius if
R is finite;
a combination of both (see below) if
R are finite.
For rings of equal number of observations,
rings selects the smallest
ring containing at least
N valid observations.
For rings of equal radius,
rings selects all clusters located at a lower
R from the central cluster.
For combination of both,
calculates firts the ring with the minimum number of observations and test if its radius is lower
R or not. If so, the ring is kept, otherwise the ring of maximum radius is calculated.
Different series of rings could be simultaneoulsy calculated by providing different values for
rings will calculate rings corresponding to each couple (N,R).
object with the slot
rings completed for each couple (N,R).
Each entry is composed of 3 elements:
N, minimum number of observations per ring;
maximum radius of rings and
estimates, a data frame with the following variables:
"id" cluster ID.
"r.pos" number of positive cases inside the ring.
"r.n" number of valid observations inside the ring.
"r.prev" observed prevalence (in \
"r.radius" ring radius (in kilometers if coordinates in decimal degrees, in the unit of the projection otherwise).
"r.clusters" number of clusters located inside the ring.
"r.wpos" (optional) sum of weights of positive cases inside the ring.
"r.wn" (optional) sum of weights of valid observations inside the ring.
"r.wprev" (optional) weighted observed prevalence (in \
Note: the list
rings is named, the name of each element is NN_value.RR_value,
for example N300.RInf.
Note 2: r.wpos, r.wn and r.wprev are calculated only if the slot
object contains weighted data.
Larmarange Joseph, Vallo Roselyne, Yaro Seydou, Msellati Philippe and Meda Nicolas (2011) "Methods for mapping regional trends of HIV prevalence from Demographic and Health Surveys (DHS)", Cybergeo : European Journal of Geography, no 558, https://journals.openedition.org/cybergeo/24606, DOI: 10.4000/cybergeo.24606.
Larmarange Joseph (2007) Prévalences du VIH en Afrique : validité d'une mesure, PhD thesis in demography, directed by Benoît Ferry, université Paris Descartes, https://tel.archives-ouvertes.fr/tel-00320283.
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