| 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 |
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 |
a |
The |
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)