Objects of class prevR.

Class used by the package prevR

Slots

clusters

data.frame with observed data (one line per cluster). Columns names are:

"id" cluster ID.
"x" longitude.
"y" latitude.
"n" number of valid observations per cluster.
"pos" number of positive cases per cluster.
"prev" observed prevalence (in %) in the cluster (pos/n).
"wn" (optional) sum of weights of observations per cluster.
"wpos" (optional) sum of weights of positive cases per cluster.
"wprev" (optional) weighted observed prevalence (in %) in the cluster (wpos/wn).
"c.type" (optional) cluster type.

boundary

object of class sf::sf, borders of the studied area.

proj

object of class sf::crs, map projection used.

rings

list of results returned by rings(). 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 %) 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.

Objects from the Class

Objects of this class could be created by the function as.prevR().

Methods

as.data.frame: signature(x = "prevR") converts an object of class prevR into a data frame.
as.SpatialGrid: signature(object = "prevR") generates a spatial grid.
export: signature(object = "prevR") exports a prevR object as a shapefile, a dbase file or a text file.
idw: signature(formula = "ANY", locations = "prevR") calculates a spatial interpolation using an inverse distance weighting.
kde: signature(object = "prevR") estimates a prevalence surface using kernel density estimators.
krige: signature(formula = "ANY", locations = "prevR") calculates a spatial interpolation by kriging.
plot: signature(x = "prevR", y = "ANY") plots data of a prevR object.
print: signature(x = "prevR") shows a summary of a prevR object.
rings: signature(object = "prevR") calculates rings of equal number of observations and/or equal radius.
show: signature(object = "prevR") shows a summary of a prevR object.
summary: signature(object = "prevR") shows a summary of the variables of a prevR object.
changeproj: signature(object = "prevR") changes the map projection used.

Examples

showClass("prevR")
#> Class "prevR" [package "prevR"]
#> 
#> Slots:
#>                                                   
#> Name:    clusters   boundary       proj      rings
#> Class: data.frame         sf        crs       list

col <- c(
  id = "cluster",
  x = "x",
  y = "y",
  n = "n",
  pos = "pos",
  c.type = "residence",
  wn = "weighted.n",
  wpos = "weighted.pos"
)
dhs <- as.prevR(fdhs.clusters, col, fdhs.boundary)
str(dhs)
#> Formal class 'prevR' [package "prevR"] with 4 slots
#>   ..@ clusters:'data.frame':	401 obs. of  10 variables:
#>   .. ..$ id    : int [1:401] 1 10 100 101 102 103 104 105 106 107 ...
#>   .. ..$ x     : num [1:401] -1.21 -1.79 -2.29 -2.71 -1.96 ...
#>   .. ..$ y     : num [1:401] 7.29 6.13 5.96 6.04 5.12 ...
#>   .. ..$ n     : num [1:401] 23 22 22 28 21 21 11 24 23 15 ...
#>   .. ..$ pos   : num [1:401] 0 0 0 0 3 4 0 1 0 0 ...
#>   .. ..$ c.type: Factor w/ 2 levels "Rural","Urban": 1 1 1 1 1 1 1 1 1 1 ...
#>   .. ..$ wn    : num [1:401] 19.8 19.8 20.2 20.2 20.2 ...
#>   .. ..$ wpos  : num [1:401] 0 0 0 0 2.88 ...
#>   .. ..$ prev  : num [1:401] 0 0 0 0 14.3 ...
#>   .. ..$ wprev : num [1:401] 0 0 0 0 14.3 ...
#>   ..@ boundary:Classes ‘sf’ and 'data.frame':	1 obs. of  1 variable:
#>   .. ..$ geometry:sfc_POLYGON of length 1; first list element: List of 1
#>   .. .. ..$ : num [1:4056, 1:2] 1.28 1.25 1.23 1.22 1.22 ...
#>   .. .. ..- attr(*, "class")= chr [1:3] "XY" "POLYGON" "sfg"
#>   .. ..- attr(*, "sf_column")= chr "geometry"
#>   .. ..- attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: 
#>   .. .. ..- attr(*, "names")= chr(0) 
#>   .. ..- attr(*, "valid")= logi TRUE
#>   ..@ proj    :List of 2
#>   .. ..$ input: chr "+proj=longlat +datum=WGS84"
#>   .. ..$ wkt  : chr "GEOGCRS[\"unknown\",\n    DATUM[\"World Geodetic System 1984\",\n        ELLIPSOID[\"WGS 84\",6378137,298.25722"| __truncated__
#>   .. ..- attr(*, "class")= chr "crs"
#>   ..@ rings   : list()
print(dhs)
#> Object of class 'prevR'
#> Number of clusters: 401
#> Number of observations: 8000
#> Number of positive cases: 810
#> The dataset is weighted.
#> 
#> National prevalence: 10.12%
#> National weighted prevalence: 10.16%
#> 
#> Projection used: +proj=longlat +datum=WGS84
#> 
#> Coordinate range
#>        min     max
#> x -5.37750  3.6850
#> y  4.80326 14.1225
#> 
#> Boundary coordinate range
#>      xmin      ymin      xmax      ymax 
#> -5.518916  4.736723  3.851701 15.082593 

if (FALSE) {
dhs <- rings(fdhs, N = c(100, 300, 500))
str(dhs)
print(dhs)
}

Slots

Objects from the Class

Methods

See also

Examples