This function estimates the ICC from mixed effects models estimated using lme4.
Usage
iccMixed(dv, id, data, family = c("gaussian", "binomial"))Arguments
- dv
A character string giving the variable name of the dependent variable.
- id
A character vector of length one or more giving the ID variable(s). Can be more than one.
- data
A data.table containing the variables used in the formula. This is a required argument. If a data.frame, it will silently coerce to a data.table. If not a data.table or data.frame, it will attempt to coerce, with a message.
- family
A character vector giving the family to use for the model. Currently only supports “gaussian” or “binomial”.
References
For details, see Campbell, M. K., Mollison, J., and Grimshaw, J. M. (2001) <doi:10.1002/1097-0258(20010215)20:3 "Cluster trials in implementation research: estimation of intracluster correlation coefficients and sample size."
Examples
iccMixed("mpg", "cyl", mtcars)
#> Var Sigma ICC
#> <char> <num> <num>
#> 1: cyl 33.19401 0.7617095
#> 2: Residual 10.38430 0.2382905
iccMixed("mpg", "cyl", data.table::as.data.table(mtcars))
#> Var Sigma ICC
#> <char> <num> <num>
#> 1: cyl 33.19401 0.7617095
#> 2: Residual 10.38430 0.2382905
iccMixed("mpg", "cyl", data.table::as.data.table(mtcars), family = "gaussian")
#> Var Sigma ICC
#> <char> <num> <num>
#> 1: cyl 33.19401 0.7617095
#> 2: Residual 10.38430 0.2382905
iccMixed("mpg", c("cyl", "am"), data.table::as.data.table(mtcars))
#> Var Sigma ICC
#> <char> <num> <num>
#> 1: cyl 26.532017 0.68284241
#> 2: am 2.921697 0.07519438
#> 3: Residual 9.401542 0.24196321
iccMixed("am", "cyl", data.table::as.data.table(mtcars), family = "binomial")
#> Var Sigma ICC
#> <char> <num> <num>
#> 1: cyl 0.9467022 0.2234596
#> 2: Residual 3.2898681 0.7765404