ESS-method: Effective sample size

In GabrielHoffman/variancePartition: Quantify and interpret drivers of variation in multilevel gene expression experiments

Effective sample size

Description

Compute effective sample size based on correlation structure in linear mixed model

Usage

ESS(fit, method = "full")

## S4 method for signature 'lmerMod'
ESS(fit, method = "full")

Arguments

fit	model fit from lmer()
method	"full" uses the full correlation structure of the model. The "approximate" method makes the simplifying assumption that the study has a mean of m samples in each of k groups, and computes m based on the study design. When the study design is evenly balanced (i.e. the assumption is met), this gives the same results as the "full" method.

Details

Effective sample size calculations are based on:

Liu, G., and Liang, K. Y. (1997). Sample size calculations for studies with correlated observations. Biometrics, 53(3), 937-47.

"full" method: if

V_x = var(Y;x)

is the variance-covariance matrix of Y, the response, based on the covariate x, then the effective sample size corresponding to this covariate is

\Sigma_{i,j} (V_x^{-1})_{i,j}

. In R notation, this is: sum(solve(V_x)). In practice, this can be evaluted as sum(w), where R

"approximate" method: Letting m be the mean number of samples per group,

k

be the number of groups, and

\rho

be the intraclass correlation, the effective sample size is

mk / (1+\rho(m-1))

Note that these values are equal when there are exactly m samples in each group. If m is only an average then this an approximation.

Value

effective sample size for each random effect in the model

Examples

library(lme4)
data(varPartData)

# Linear mixed model
fit <- lmer(geneExpr[1, ] ~ (1 | Individual) + (1 | Tissue) + Age, info)

# Effective sample size
ESS(fit)

GabrielHoffman/variancePartition documentation built on April 20, 2024, 7:29 p.m.