Implementation of the Green's Index of Dispersion by bootstrap. The sampling distribution of the Green's Index is not well described hence bootstrapping is used to test whether the distribution of cases across primary sampling units is random.

greensIndex(data, psu, case, replicates = 999)

## Arguments

data Survey dataset (as an R data.frame) Name of variable holding PSU (cluster) data as a character vector of length = 1 (e.g. psu) Name of variable holding case status as a character vector of length = 1 (e.g. GAM). The function assumes that case status is coded with 1 = case Number of bootstrap replicates (default is 9999)

## Value

A list of class GI with names:

 Variable Description GI Estimate of Green's index LCL 95\% LCL for GI UCL 95\% UCL for GI minGI Minimum possible GI (maximum uniformity) for the data p p-value (H0: = Random distribution of cases across PSUs)

## Details

The value of Green's Index can range between -1/(n - 1) for maximum uniformity (specific to the dataset) and one for maximum clumping. The interpretation of Green’s Index is straightforward:

 Green's Index Value Interpretation Green's Index close to 0 Random Green's Index greater than 0 Clumped (i.e. more clumped than random) Green’s Index less than 0 Uniform (i.e. more uniform than random)

## Examples

# Apply Green's Index using anthropometric data from a SMART survey in Sudan
# (flag.ex01)
svy <- flag.ex01
svy$flag <- 0 svy$flag <- ifelse(!is.na(svy$haz) & (svy$haz < -6 | svy$haz > 6), svy$flag + 1, svy$flag) svy$flag <- ifelse(!is.na(svy$whz) & (svy$whz < -5 | svy$whz > 5), svy$flag + 2, svy$flag) svy$flag <- ifelse(!is.na(svy$waz) & (svy$waz < -6 | svy$waz > 5), svy$flag + 4, svy$flag) svy <- svy[svy$flag == 0, ]
svy$stunted <- ifelse(svy$haz < -2, 1, 2)

## set seed to 0 to replicate results
set.seed(0)
greensIndex(data = svy, psu = "psu", case = "stunted")
#>
#> 	Green's Index of Dispersion
#>
#> Green's Index (GI) of Dispersion  = -0.0014, 95% CI = (-0.0021, -0.0005)
#> Maximum uniformity for this data  = -0.0035
#>                          p-value  =  0.0030
#>