GEE Gaussian regression {Rfast2}R Documentation

GEE Gaussian regression

Description

GEE Gaussian regression.

Usage

gee.reg(y, x, id, tol = 1e-07, maxiters = 100)

Arguments

y

The dependent variable, a numerical vector.

x

A matrix with the indendent variables.

id

A numerical variable with 1, 2, ... indicating the subject. Unbalanced design is of course welcome.

tol

The tolerance value to terminate the Newton-Raphson algorithm. This is set to 10^{-7} by default.

maxiters

The maximum number of iterations that can take place during the fitting.

Details

Gaussin GEE regression is fitted.

Value

A list including:

be

The regression coefficients.

seb

The standard errors of the regression coefficients.

phi

The \phi parameter.

a

The \alpha parameter.

covbeta

The covariance matrix of the regression coefficients.

iters

The number of iteration the Newton-Raphson required.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

References

Wang M. (2014). Generalized estimating equations in longitudinal data analysis: a review and recent developments. Advances in Statistics, 2014.

Hardin J. W. and Hilbe J. M. (2002). Generalized estimating equations. Chapman and Hall/CRC.

See Also

cluster.lm, fe.lmfit, wild.boot, fipois.reg

Examples


y <- rnorm(200)
id <- sample(1:20, 200, replace = TRUE)
x <- rnorm(200, 3)
gee.reg(y, x, id)


[Package Rfast2 version 0.1.5.2 Index]