Allison, P. D. (1995). The impact of random predictors on comparisons
of coefficients between models: Comment on Clogg, Petkova, and
Haritou. American Journal of Sociology, 100(5),
1294–1305.
Clogg, C. C., Petkova, E., and Haritou, A. (1995). Statistical methods
for comparing regression coefficients between models.
American Journal of Sociology, 100(5), 1261–1293.
Yan, J., Aseltine, R., and Harel, O. (2011). Comparing Regression
Coefficients Between Nested Linear Models for Clustered Data with
Generalized Estimating Equations. Journal of Educational and
Behaviorial Statistics, Forthcoming.
Examples
## generate clustered data
gendat <- function(ncl, clsz) {
## ncl: number of clusters
## clsz: cluster size (all equal)
id <- rep(1:ncl, each = clsz)
visit <- rep(1:clsz, ncl)
n <- ncl * clsz
x1 <- rbinom(n, 1, 0.5) ## within cluster varying binary covariate
x2 <- runif(n, 0, 1) ## within cluster varying continuous covariate
## the true correlation coefficient rho for an ar(1)
## correlation structure is 2/3
rho <- 2/3
rhomat <- rho ^ outer(1:4, 1:4, function(x, y) abs(x - y))
chol.u <- chol(rhomat)
noise <- as.vector(sapply(1:ncl, function(x) chol.u %*% rnorm(clsz)))
y <- 1 + 3 * x1 - 2 * x2 + noise
dat <- data.frame(y, id, visit, x1, x2)
dat
}
simdat <- gendat(100, 4)
fit0 <- geese(y ~ x1, id = id, data = simdat, corstr = "un")
fit1 <- geese(y ~ x1 + x2, id = id, data = simdat, corstr = "un")
compCoef(fit0, fit1)