rings,prevR-method | R Documentation |

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.

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

`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? |

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`

).

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
N*N_value*.R*R_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.

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.

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

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