binnest {repeated} | R Documentation |

`binnest`

is designed to handle binary and binomial data with two
levels of nesting. The first level is the individual and the second
will consist of clusters within individuals.

The variance components at the two levels can only depend on the
covariates if `response`

has class, `repeated`

.

binnest(response, totals=NULL, nest=NULL, ccov=NULL, tvcov=NULL, mu=~1, re1=~1, re2=~1, preg=NULL, pre1=NULL, pre2=NULL, binom.mix=c(10,10), binom.prob=c(0.5,0.5), fcalls=900, eps=0.01, print.level=0)

`response` |
A list of three column matrices with counts,
corresponding totals (not necessary if the response is binary), and
(second-level) nesting indicator for each individual, one matrix or
dataframe of such counts, or an object of class, response (created by
`restovec` ) or repeated (created by
`rmna` ). |

`totals` |
If `response` is a matrix or dataframe, a
corresponding matrix or dataframe of totals (not necessary if the
response is binary). Ignored otherwise. |

`nest` |
If `response` is a matrix or dataframe, a
corresponding matrix or dataframe of nesting indicators. Ignored
otherwise. |

`ccov` |
If `response` is a matrix, dataframe, list, or object
of class, `response` , a matrix of time-constant covariates or an
object of class, `tccov` (created by `tcctomat` ). All
of these covariates are used in the fixed effects part of the model.
Ignored if response has class, `repeated` . |

`tvcov` |
If `response` is a matrix, dataframe, list, or object
of class, `response` , an object of class, `tvcov` (created
by `tvctomat` ). All of these covariates are used in the
fixed effects part of the model. Ignored if response has class,
`repeated` . |

`mu` |
If `response` has class, `repeated` , a formula
beginning with ~, specifying a linear regression function for the
fixed effects, in the Wilkinson and Rogers notation, containing
selected covariates in the response object. (A logit link is
assumed.) |

`re1` |
If `response` has class, `repeated` , a formula
beginning with ~, specifying a linear regression function for the
variance of the first level of nesting, in the Wilkinson and Rogers
notation, containing selected covariates in the response object. If
NULL, a random effect is not fitted at this level. (A log link is
assumed.) |

`re2` |
If `response` has class, `repeated` , a formula
beginning with ~, specifying a linear regression function for the
variance of the second level of nesting, in the Wilkinson and Rogers
notation, containing selected covariates in the response object. If
NULL, a random effect is not fitted at this level. (A log link is
assumed.) |

`preg` |
Initial parameter estimates for the fixed effect
regression model: either the model specified by `mu` or else the
intercept plus one for each covariate in `ccov` and
`tvcov` . |

`pre1` |
Initial parameter estimates for the first level of nesting
variance model: either the model specified by `re1` or just the
intercept. If NULL, a random effect is not fitted at this level. |

`pre2` |
Initial parameter estimates for the second level of nesting
variance model: either the model specified by `re1` or just the
intercept. If NULL, a random effect is not fitted at this level. |

`binom.mix` |
A vector of two values giving the totals for the binomial distributions used as the mixing distributions at the two levels of nesting. |

`binom.prob` |
A vector of two values giving the probabilities in the binomial distributions used as the mixing distributions at the two levels of nesting. If they are 0.5, the mixing distributions approximate normal mixing distributions; otherwise, they are skewed. |

`fcalls` |
Number of function calls allowed. |

`eps` |
Convergence criterion. |

`print.level` |
If 1, the iterations are printed out. |

A list of classes `binnest`

is returned.

T.R. Ten Have and J.K. Lindsey

Ten Have, T.R., Kunselman, A.R., and Tran, L. (1999) Statistics in Medicine 18, 947-960.

`gar`

, `read.list`

,
`restovec`

, `rmna`

,
`tcctomat`

, `tvctomat`

.

#y <- rbind(matrix(rbinom(20,1,0.6), ncol=4), # matrix(rbinom(20,1,0.4), ncol=4)) y <- matrix(c(1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1,0,1,1,0,0,1,0,1,1,0,1,0, 1,1,1,1,1,1,1,1,0,1,1,0),nrow=10,ncol=4,byrow=TRUE) resp <- restovec(y, nest=1:4, times=FALSE) ccov <- tcctomat(c(rep(0,5),rep(1,5)), name="treatment") reps <- rmna(resp, ccov=ccov) # two random effects binnest(reps, mu=~treatment, preg=c(1,0), pre1=1, pre2=1) # first level random effect only binnest(reps, mu=~treatment, preg=c(1,-1), pre1=1) # second level random effect only binnest(reps, mu=~treatment, preg=c(1,-1), pre2=1)

[Package *repeated* version 1.0 Index]