nEffective: Estimate the effective sample size from longitudinal data

In multilevelTools: Multilevel and Mixed Effects Model Diagnostics and Effect Sizes

Description

This function estimates the (approximate) effective sample size.

Usage

nEffective(n, k, icc, dv, id, data, family = c("gaussian", "binomial"))

Arguments

n	The number of unique/indepedent units of observation
k	The (average) number of observations per unit
icc	The estimated ICC. If missing, will estimate (and requires that the family argument be correctly specified).
dv	A character string giving the variable name of the dependent variable.
id	A character vector of length one giving the ID variable.
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”.

Value

A data.table including the effective sample size.

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

## example where n, k, and icc are estimated from the data
## provided, partly using iccMixed function
nEffective(dv = "mpg", id = "cyl", data = mtcars)

## example where n, k, and icc are known (or being 'set')
## useful for sensitivity analyses
nEffective(n = 60, k = 10, icc = .6)

multilevelTools documentation built on March 13, 2020, 2:07 a.m.