nEffective: Estimate the effective sample size from longitudinal data

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

Estimate the effective sample size from longitudinal data

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)

JWiley/multilevelTools documentation built on April 1, 2024, 9:56 p.m.