R: Compare Regression Coefficiente between Nested Models

compCoef {geepack} | R Documentation

## Compare Regression Coefficiente between Nested Models

### Description

Comparing regression coefficients between models when one model is nested within another for clustered data.

### Usage

    compCoef(fit0, fit1)

### Arguments

`fit0` | a fitted object of class `geese`
`fit1` | another fitted object of class `geese`

### Value

a list of two components:

`delta` | estimated difference in the coefficients of common covariates from `fit0` and `fit1`
`variance` | estimated variance matrix of delta

### Author(s)

Jun Yan jyan.stat@gmail.com

### References

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)

[Package geepack version 1.2-1 Index]