Description Usage Arguments Details Value Author(s) References Examples

This function groups the observations in a binomial glm based on the covariate structure. This can make it possible to assess goodness-of-fit in some models fitted to binary observations.

1 2 |

`object` |
a binomial glm object |

`eval` |
should the new glm-model be evaluated? |

`ind` |
an indicator for which rows to keep. If this is not specified the grouping structure is based on the covariate structure in the model. |

`...` |
currently not used |

The residual deviance and residual Pearson deviance are not meaningful measures of goodness-of-fit if the expected frequencies under the model are small (say less than five).

if `eval = TRUE`

it is tested whether the estimated coefficients
are identical up to three significant digits and a warning is issued
if this is not the case. This should be the case in well-behaved
situations but may not happen in cases of complete separation.

A list with components

`newCall` |
the new call |

`newData` |
a data frame with the aggregated data set |

`oldData` |
a data frame with the original data set |

`oldN` |
the number of rows (cases / observations) in the original data set |

`newN` |
the number of rows (cases / observations) in the aggregated data set |

`oldObject` |
the original fitted model |

`newObject` |
if |

Rune Haubo B Christensen

Collett, D. (2003) *Modelling binary data*. Second
edition. Chapman & Hall/CRC.

Venables, W.N. and Ripley, B.D. (2002) *Modern Applied Statistics
with S*. Fourth Edition. Springer

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
## Lifted from example(predict.glm):
ldose <- rep(0:5, 2)
numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
sex <- factor(rep(c("M", "F"), c(6, 6)))
SF <- cbind(numdead, numalive=20-numdead)
## budworm.lg <- glm(SF ~ sex*ldose, family=binomial)
## summary(budworm.lg)
dat <- data.frame(SF=SF, sex, ldose)
dat[10, 1:2] <- rep(5, 2)
dat[13, ] <- dat[10, ]
rm(SF, sex, ldose)
SF <- as.matrix(dat[,1:2])
dat <- dat[,-(1:2)]
dat <- as.data.frame(cbind(SF, dat))
summary(m0 <- glm(SF ~ sex*ldose, binomial, dat))
## Various types of grouping:
(ind <- c(1:12, 10))
g <- group(m0, ind=ind, eval=TRUE)
g <- group(m0, eval=FALSE)
g <- group(m0, eval=TRUE)
## The correct GOF-test from the residual deviance is given by:
g$newObject
``` |

binomTools documentation built on May 29, 2017, 10:12 p.m.

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