# 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

 rings,prevR-method R Documentation

## Calculation of rings of equal number of observation and/or equal radius.

### 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

``````## 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 row of the data frame `clusters` of `object`, `rings()` determines a ring, centered on the cluster. It could be:

• rings of equal 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 both 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 first 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 simultaneously 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 %) inside the ring (r.pos/r.n).

• "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 %) inside the ring (r.wpos/r.wn).

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://theses.hal.science/tel-00320283.

prevR.

### Examples

``````## Not run:
print(fdhs)
dhs <- rings(fdhs, N = c(100, 200, 300, 400, 500))
print(dhs)

## End(Not run)
``````

prevR documentation built on May 31, 2023, 7:32 p.m.