glmm {repeated} | R Documentation |

`glmm`

fits a generalized linear mixed model with a random
intercept using a normal mixing distribution computed by Gauss-Hermite
integration. For the normal, gamma, and inverse Gaussian
distributions, the deviances supplied are -2 log likelihood, not the
usual `glm`

deviance; the degrees of freedom take into
account estimation of the dispersion parameter.

If weights and/or offset are to be used or the formula transforms
some variables, all of the data must be supplied in a dataframe.
Because the `glm`

function is such a hack, if this is not
done, weird error messages will result.

na.omit is not allowed.

glmm(formula, family=gaussian, data=list(), weights=NULL, offset=NULL, nest, delta=1, maxiter=20, points=10, print.level=0, control=glm.control(epsilon=0.0001,maxit=10,trace=FALSE))

`formula` |
A symbolic description of the model to be fitted. If it contains transformations of the data, including cbind for binomial data, a dataframe must be supplied. |

`family` |
A description of the error distribution and link
function to be used in the model; see `family` for details. |

`data` |
A dataframe containing the variables in the model, that is optional in simple cases, but required in certain situations as specified elsewhere in this help page. |

`weights` |
An optional weight vector. If this is used, data must be supplied in a data.frame. |

`offset` |
The known component in the linear predictor. If this is used, data must be supplied in a data.frame. An offset cannot be specified in the model formula. |

`nest` |
The variable classifying observations by the unit (cluster) upon which they were observed. |

`delta` |
If the response variable has been transformed, this is the Jacobian of that transformation, so that AICs are comparable. |

`maxiter` |
The maximum number of iterations of the outer loop for numerical integration. |

`points` |
The number of points for Gauss-Hermite integration of the random effect. |

`print.level` |
If set equal to 2, the log probabilities are printed out when the underflow error is given. |

`control` |
A list of parameters for controlling the fitting process. |

`glmm`

returns a list of class `glmm`

J.K. Lindsey

`family`

, `fmr`

, `glm`

,
`glm.control`

, `gnlmix`

,
`gnlmm`

, `gnlr`

,
`gnlr3`

, `hnlmix`

,
`nls`

.

# Poisson counts nest <- gl(5,4) y <- rpois(20,5+2*as.integer(nest)) # overdispersion model glmm(y~1, family=poisson, nest=gl(20,1), points=3) # clustered model glmm(y~1, family=poisson, nest=nest, points=3) # # binomial data with model for overdispersion df <- data.frame(r=rbinom(10,10,0.5), n=rep(10,10), x=c(rep(0,5), rep(1,5)), nest=1:10) glmm(cbind(r,n-r)~x, family=binomial, nest=nest, data=df)

[Package *repeated* version 1.0 Index]