# rings-prevR-method: Calculation of rings of equal number of observation and/or... In prevR: Estimating Regional Trends of a Prevalence from a DHS and Similar Surveys

## Description

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.

## Usage

 ```1 2``` ```## S4 method for signature 'prevR' rings(object, N = seq(100, 500, 50), R = Inf, progression = TRUE) ```

## Arguments

 `object` object of class prevR. `N` minimum number of observations. `R` maximum rings radius (in kilometers if coordinates in decimal degrees, in the unit of the projection otherwise). `progression` show a progress bar?

## Details

For each ligne of the data frame `clusters` of `object`, `rings` determines a ring, centred on the cluster. It could be:

• rings of eaqul number of observations if `N` is finite and `R=Inf`;

• rings of equal radius if `N=Inf` and `R` is finite;

• a combination of both (see below) if `N` and `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 distance than `R` from the central cluster.
For combination of both, `rings` calculates firts the ring with the minimum number of observations and test if its radius is lower than `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 `N` and `R`. `rings` will calculate rings corresponding to each couple (N,R).

## Value

Return `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; `R`, 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 `clusters` of `object` contains weighted data.

## References

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.

 ```1 2 3 4 5 6``` ```## Not run: print(fdhs) dhs <- rings(fdhs,N=c(100,200,300,400,500)) print(dhs) ## End(Not run) ```