View source: R/regression_models_for_clustered_data.R

GEE Gaussian regression | R Documentation |

GEE Gaussian regression.

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

`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. |

Gaussin GEE regression is fitted.

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. |

Michail Tsagris.

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

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.

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

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

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