Introduction to prevR

Joseph Larmarange

This package performs a methodological approach for spatial estimation of regional trends of a prevalence using data from surveys using a stratified two-stage sample design (as Demographic and Health Surveys). In these kind of surveys, positive and control cases are spatially positioned at the centre of their corresponding surveyed cluster.

This package provides functions to estimate a prevalence surface using a kernel estimator with adaptative bandwiths of equal number of persons surveyed (a variant of the nearest neighbour technique) or with fixed bandwiths. The prevalence surface could also be calculated using a spatial interpolation (kriging or inverse distance weighting) after a moving average smoothing based on circles of equal number of observed persons or circles of equal radius.

With the kernel estimator approach, it’s also possible to estimate a surface of relative risks.

The methodological approach has been described in:

Application to generate HIV prevalence surfaces can be found at:

Other papers using prevR could be found on Google Scholar.

Importing data

To create a prevR object, you need three elements:

library(prevR, quietly = TRUE)
## Registered S3 methods overwritten by 'ggplot2':
##   method         from 
##   [.quosures     rlang
##   c.quosures     rlang
##   print.quosures rlang
## Registered S3 method overwritten by 'xts':
##   method     from
##   as.zoo.xts zoo
## 
## 
## Welcome to 'prevR': estimate regional trends of a prevalence.
##  - type help('prevR') for details
##  - type demo(prevR) for a demonstration
##  - type citation('prevR') to cite prevR in a publication.
## 
## 
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
##   [email protected] 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 ...
##   [email protected] boundary:Formal class 'SpatialPolygons' [package "sp"] with 4 slots
##   .. .. [email protected] polygons   :List of 1
##   .. .. .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
##   .. .. .. .. .. [email protected] Polygons :List of 1
##   .. .. .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
##   .. .. .. .. .. .. .. .. [email protected] labpt  : num [1:2] -0.794 10.172
##   .. .. .. .. .. .. .. .. [email protected] area   : num 51.9
##   .. .. .. .. .. .. .. .. [email protected] hole   : logi FALSE
##   .. .. .. .. .. .. .. .. [email protected] ringDir: int 1
##   .. .. .. .. .. .. .. .. [email protected] coords : num [1:4056, 1:2] 1.28 1.25 1.23 1.22 1.22 ...
##   .. .. .. .. .. [email protected] plotOrder: int 1
##   .. .. .. .. .. [email protected] labpt    : num [1:2] -0.794 10.172
##   .. .. .. .. .. [email protected] ID       : chr "P1"
##   .. .. .. .. .. [email protected] area     : num 51.9
##   .. .. [email protected] plotOrder  : int 1
##   .. .. [email protected] bbox       : num [1:2, 1:2] -5.52 4.74 3.85 15.08
##   .. .. .. ..- attr(*, "dimnames")=List of 2
##   .. .. .. .. ..$ : chr [1:2] "x" "y"
##   .. .. .. .. ..$ : chr [1:2] "min" "max"
##   .. .. [email protected] proj4string:Formal class 'CRS' [package "sp"] with 1 slot
##   .. .. .. .. [email protected] projargs: chr "+proj=longlat +ellps=WGS84"
##   [email protected] proj    :Formal class 'CRS' [package "sp"] with 1 slot
##   .. .. [email protected] projargs: chr "+proj=longlat +ellps=WGS84"
##   [email protected] 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 +ellps=WGS84
## 
## Coordinate range
##        min     max
## x -5.37750  3.6850
## y  4.80326 14.1225
## 
## Boundary coordinate range
##         min       max
## x -5.518916  3.851701
## y  4.736723 15.082593

An interactive helper function import.dhs() could be used to compute statistics per cluster and to generate the prevR object for those who downloaded individual files (SPSS format) and location of clusters (dbf format) from DHS website (https://dhsprogram.com/).

imported_data <- import.dhs("data.sav", "gps.dbf")

Boudaries of a specific country could be obtained with create.boundary().

Plotting a prevR object

plot(dhs, main = "Clusters position")

plot(dhs, type = "c.type", main = "Clusters by residence")

plot(dhs, type = "count", main = "Observations by cluster")

plot(dhs, type = "flower", main = "Positive cases by cluster")

Changing coordinates projection

plot(dhs, axes = TRUE)

dhs <- changeproj(dhs, "+proj=utm +zone=30 +ellps=WGS84 +datum=WGS84 +units=m +no_defs")
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=utm +zone=30 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +towgs84=0,0,0
## 
## Coordinate range
##        min     max
## x 240094.2 1231995
## y 531003.3 1562155
## 
## Boundary coordinate range
##        min     max
## x 224228.1 1251165
## y 523628.1 1669034
plot(dhs, axes = TRUE)

Quick analysis

Function quick.prevR() allows to perform a quick analysis:

quick.prevR(fdhs)

Several values of N could be specified, and several options allows you to return detailed results.

res <- quick.prevR(fdhs, N = c(100, 200, 300), return.results = TRUE, return.plot = TRUE, plot.results = FALSE, progression = FALSE, nb.cells = 50)
res$plot

Step by step analysis

# Calculating rings of the same number of observations for different values of N
dhs <- rings(fdhs, N = c(100, 200, 300, 400, 500), progression = FALSE)
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 +ellps=WGS84
## 
## Coordinate range
##        min     max
## x -5.37750  3.6850
## y  4.80326 14.1225
## 
## Boundary coordinate range
##         min       max
## x -5.518916  3.851701
## y  4.736723 15.082593
## 
## Available (N,R) couples in the slot 'rings':
##    N   R
##  100 Inf
##  200 Inf
##  300 Inf
##  400 Inf
##  500 Inf
summary(dhs)
## Object of class 'prevR'
## SLOT CLUSTERS
##        x                 y                n              pos      
##  Min.   :-5.3775   Min.   : 4.803   Min.   : 8.00   Min.   :0.00  
##  1st Qu.:-1.7925   1st Qu.: 6.375   1st Qu.:17.00   1st Qu.:0.00  
##  Median :-0.7650   Median : 7.455   Median :20.00   Median :2.00  
##  Mean   :-0.6605   Mean   : 8.647   Mean   :19.95   Mean   :2.02  
##  3rd Qu.: 0.1590   3rd Qu.:11.205   3rd Qu.:23.00   3rd Qu.:3.00  
##  Max.   : 3.6850   Max.   :14.123   Max.   :31.00   Max.   :9.00  
##    c.type          wn             wpos            prev       
##  Rural:230   Min.   :18.58   Min.   :0.000   Min.   : 0.000  
##  Urban:171   1st Qu.:19.84   1st Qu.:0.000   1st Qu.: 0.000  
##              Median :20.04   Median :1.544   Median : 7.692  
##              Mean   :19.95   Mean   :2.027   Mean   :10.143  
##              3rd Qu.:20.12   3rd Qu.:3.166   3rd Qu.:15.789  
##              Max.   :21.76   Max.   :8.806   Max.   :43.750  
##      wprev       
##  Min.   : 0.000  
##  1st Qu.: 0.000  
##  Median : 7.692  
##  Mean   :10.143  
##  3rd Qu.:15.789  
##  Max.   :43.750
## 
## SLOT RINGS FOR N=100 AND R=Inf
##      r.pos            r.n            r.prev          r.radius      
##  Min.   : 0.00   Min.   :100.0   Min.   : 0.000   Min.   :  4.155  
##  1st Qu.: 4.00   1st Qu.:105.0   1st Qu.: 4.000   1st Qu.: 23.046  
##  Median :11.00   Median :110.0   Median : 9.483   Median : 37.853  
##  Mean   :11.63   Mean   :110.7   Mean   :10.550   Mean   : 42.219  
##  3rd Qu.:18.00   3rd Qu.:115.0   3rd Qu.:15.789   3rd Qu.: 57.861  
##  Max.   :32.00   Max.   :127.0   Max.   :27.586   Max.   :142.042  
##    r.clusters        r.wpos            r.wn           r.wprev      
##  Min.   :4.000   Min.   : 0.000   Min.   : 79.76   Min.   : 0.000  
##  1st Qu.:5.000   1st Qu.: 4.515   1st Qu.:100.25   1st Qu.: 3.895  
##  Median :6.000   Median :11.175   Median :118.70   Median : 9.551  
##  Mean   :5.591   Mean   :11.792   Mean   :111.52   Mean   :10.684  
##  3rd Qu.:6.000   3rd Qu.:17.256   3rd Qu.:120.13   3rd Qu.:15.735  
##  Max.   :7.000   Max.   :33.937   Max.   :140.88   Max.   :28.210
## 
## SLOT RINGS FOR N=200 AND R=Inf
##      r.pos            r.n            r.prev           r.radius      
##  Min.   : 2.00   Min.   :200.0   Min.   : 0.8929   Min.   :  7.171  
##  1st Qu.: 9.00   1st Qu.:206.0   1st Qu.: 4.3902   1st Qu.: 37.579  
##  Median :22.00   Median :211.0   Median :10.2804   Median : 58.657  
##  Mean   :22.55   Mean   :210.8   Mean   :10.7053   Mean   : 64.005  
##  3rd Qu.:33.00   3rd Qu.:216.0   3rd Qu.:15.4229   3rd Qu.: 89.381  
##  Max.   :56.00   Max.   :226.0   Max.   :26.2136   Max.   :231.980  
##    r.clusters        r.wpos           r.wn          r.wprev      
##  Min.   : 9.00   Min.   : 2.47   Min.   :175.0   Min.   : 1.030  
##  1st Qu.:10.00   1st Qu.:10.50   1st Qu.:199.8   1st Qu.: 4.563  
##  Median :11.00   Median :22.30   Median :217.3   Median :10.485  
##  Mean   :10.53   Mean   :22.66   Mean   :210.0   Mean   :10.824  
##  3rd Qu.:11.00   3rd Qu.:31.98   3rd Qu.:220.0   3rd Qu.:15.797  
##  Max.   :12.00   Max.   :53.47   Max.   :241.0   Max.   :26.666
## 
## SLOT RINGS FOR N=300 AND R=Inf
##      r.pos            r.n            r.prev          r.radius      
##  Min.   : 5.00   Min.   :300.0   Min.   : 1.587   Min.   :  9.971  
##  1st Qu.:15.00   1st Qu.:304.0   1st Qu.: 4.983   1st Qu.: 45.750  
##  Median :32.00   Median :310.0   Median :10.559   Median : 73.931  
##  Mean   :33.37   Mean   :309.8   Mean   :10.764   Mean   : 79.767  
##  3rd Qu.:47.00   3rd Qu.:315.0   3rd Qu.:15.142   3rd Qu.:108.783  
##  Max.   :78.00   Max.   :327.0   Max.   :24.759   Max.   :268.172  
##    r.clusters        r.wpos            r.wn          r.wprev      
##  Min.   :13.00   Min.   : 4.284   Min.   :260.6   Min.   : 1.532  
##  1st Qu.:15.00   1st Qu.:15.937   1st Qu.:299.2   1st Qu.: 5.080  
##  Median :15.00   Median :33.525   Median :301.8   Median :10.319  
##  Mean   :15.44   Mean   :33.297   Mean   :307.9   Mean   :10.853  
##  3rd Qu.:16.00   3rd Qu.:46.856   3rd Qu.:320.0   3rd Qu.:15.429  
##  Max.   :17.00   Max.   :76.990   Max.   :341.4   Max.   :25.273
## 
## SLOT RINGS FOR N=400 AND R=Inf
##      r.pos            r.n            r.prev          r.radius     
##  Min.   : 8.00   Min.   :400.0   Min.   : 2.000   Min.   : 12.70  
##  1st Qu.:22.00   1st Qu.:405.0   1st Qu.: 5.327   1st Qu.: 54.42  
##  Median :44.00   Median :410.0   Median :10.602   Median : 85.41  
##  Mean   :44.18   Mean   :410.3   Mean   :10.764   Mean   : 94.79  
##  3rd Qu.:58.00   3rd Qu.:415.0   3rd Qu.:14.217   3rd Qu.:127.73  
##  Max.   :98.00   Max.   :427.0   Max.   :23.278   Max.   :293.64  
##    r.clusters        r.wpos            r.wn          r.wprev      
##  Min.   :18.00   Min.   : 8.229   Min.   :360.1   Min.   : 2.045  
##  1st Qu.:20.00   1st Qu.:22.358   1st Qu.:399.9   1st Qu.: 5.345  
##  Median :21.00   Median :43.851   Median :415.4   Median :10.315  
##  Mean   :20.54   Mean   :44.298   Mean   :409.6   Mean   :10.851  
##  3rd Qu.:21.00   3rd Qu.:58.963   3rd Qu.:421.0   3rd Qu.:14.341  
##  Max.   :22.00   Max.   :95.591   Max.   :443.4   Max.   :23.452
## 
## SLOT RINGS FOR N=500 AND R=Inf
##      r.pos             r.n            r.prev          r.radius     
##  Min.   : 14.00   Min.   :500.0   Min.   : 2.783   Min.   : 16.38  
##  1st Qu.: 31.00   1st Qu.:505.0   1st Qu.: 6.163   1st Qu.: 67.01  
##  Median : 54.00   Median :510.0   Median :10.700   Median : 98.47  
##  Mean   : 55.24   Mean   :510.3   Mean   :10.811   Mean   :107.68  
##  3rd Qu.: 70.00   3rd Qu.:515.0   3rd Qu.:13.699   3rd Qu.:140.71  
##  Max.   :116.00   Max.   :528.0   Max.   :22.612   Max.   :347.09  
##    r.clusters        r.wpos            r.wn          r.wprev      
##  Min.   :23.00   Min.   : 12.93   Min.   :455.7   Min.   : 2.499  
##  1st Qu.:25.00   1st Qu.: 31.71   1st Qu.:499.5   1st Qu.: 6.138  
##  Median :26.00   Median : 51.91   Median :510.9   Median :10.222  
##  Mean   :25.53   Mean   : 55.12   Mean   :509.3   Mean   :10.869  
##  3rd Qu.:26.00   3rd Qu.: 70.17   3rd Qu.:520.8   3rd Qu.:13.929  
##  Max.   :28.00   Max.   :110.78   Max.   :555.8   Max.   :22.822
## 
## QUANTILES OF r.radius (in kilometers):
##              0%   10%   25%   50%    75%    80%    90%    95%    99%
## N100.RInf  4.15  7.84 23.05 37.85  57.86  62.99  79.63  93.12 121.77
## N200.RInf  7.17 14.58 37.58 58.66  89.38  94.40 114.97 134.81 173.37
## N300.RInf  9.97 18.75 45.75 73.93 108.78 114.65 138.17 159.10 211.31
## N400.RInf 12.70 31.42 54.42 85.41 127.73 136.67 163.91 177.11 241.44
## N500.RInf 16.38 41.15 67.01 98.47 140.71 156.53 181.92 201.87 286.18
##             100%
## N100.RInf 142.04
## N200.RInf 231.98
## N300.RInf 268.17
## N400.RInf 293.64
## N500.RInf 347.09
# Prevalence surface for N=300
prev.N300 <- kde(dhs, N = 300, nb.cells = 200, progression = FALSE)
library(sp)
spplot(prev.N300, "k.wprev.N300.RInf", cuts = 100, col.regions = prevR.colors.red(101), main = "Regional trends of prevalence (N=300)")

# Surface of rings' radius
radius.N300 <- krige("r.radius", dhs, N = 300, nb.cells = 200)
## [using ordinary kriging]
spplot(radius.N300, cuts = 100, col.regions = prevR.colors.blue(101), main = "Radius of circle (N=300)")

# ggplot2 graph
res <- as.data.frame(prev.N300)
res <- res[!is.na(res$k.wprev.N300.RInf), ]
library(ggplot2)
ggplot(data = res) +
  aes(x = x, y = y, fill = k.wprev.N300.RInf) +
  geom_raster() +
  scale_fill_gradientn(colours=prevR.colors.red(20)) +
  coord_fixed() +
  theme_prevR_light() +
  labs(fill = "Prevalence (%)")

Functions and methods provided by prevR

The content of prevR can be broken up as follows:

Datasets

Creating objects

prevR functions takes as input ojects of class prevR.

Data visualisation

Data manipulation

Data analysis

Results visualisation and export