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The goal of iccmult is to estimate the intracluster correlation
coefficient (ICC) of clustered categorical response data. It provides
two estimation methods, a resampling based estimator and the method of
moments estimator. These are obtained by specifying a method in the
function iccmulti::iccmult(). This package also includes a
function to generate simulated clustered categorical response data:
iccmulti::rccat().
You can install iccmult from within R or RStudio with:
install.packages("iccmult")Alternatively, install the package from GitHub with:
# install.packages("pak")
pak::pak("ncs14/iccmult")This is a basic example which shows you how to generate clustered
categorical response data. The response probabilities must sum 1 and the
desired ICC must be a value between 0 and 1. The number of clusters is
set to 20 and each cluster is of size 25. The output of
rccat is a two column data frame of a cluster identifier
and a categorical response vector.
library(iccmult)
clustdat3 <- rccat(rho=0.25, prop=c(0.2,0.3,0.5), noc=20, csize=25)The iccmulti() function is called as follows to estimate
the ICC on the resulting data frame. The function expects two variables:
a cluster identifier and the categorical response vector. The call below
requests both the resampling and the moments estimates.
iccclust <- iccmulti(cid, y, clustdat3, method=c("rm","mom"))
#> Warning in iccmulti(cid, y, clustdat3, method = c("rm", "mom")): ICC not
#> estimable by 'Resampling' method for at least one categorical calculation
#> Warning in iccmulti(cid, y, clustdat3, method = c("rm", "mom")): Resampling
#> based confidence interval for ICC is not estimable for at least one categorical
#> calculation
#> Warning in iccmulti(cid, y, clustdat3, method = c("rm", "mom")): Resampling
#> based confidence interval for categorical ICC is not estimable
#> Warning in iccmulti(cid, y, clustdat3, method = c("rm", "mom")): Categorical
#> ICC not estimable by 'Resampling' methodThe result is a list of length two, each component holding the estimated ICC, se(ICC), and confidence interval bounds from each estimation method.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.