JWileymisc (version 0.3.1)

nEffective: 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., & Grimshaw, J. M. (2001). Cluster trials in implementation research: estimation of intracluster correlation coefficients and sample size. Statistics in Medicine, 20(3), 391-399.

Examples

# NOT RUN {
## 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)
# }