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)

psu

Name of variable holding PSU (cluster) data as a character vector of length = 1 (e.g. psu)

case

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

replicates

Number of bootstrap replicates (default is 9999)

Value

A list of class GI with names:

VariableDescription
GIEstimate of Green's index
LCL95\% LCL for GI
UCL95\% UCL for GI
minGIMinimum possible GI (maximum uniformity) for the data
pp-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 ValueInterpretation
Green's Index close to 0Random
Green's Index greater than 0Clumped (i.e. more clumped than random)
Green’s Index less than 0Uniform (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 #>