To calculate exact probability of familial clustering of disease

1 | ```
pfc(famdata,enum)
``` |

`famdata` |
collective information of sib size, number of affected sibs and their frequencies |

`enum` |
a switch taking value 1 if all possible tables are to be enumerated |

The returned value is a list containing (tailp,sump,nenum are only available if enum=1):

`p` |
the probabitly of familial clustering |

`stat` |
the deviances, chi-squares based on binomial and hypergeometric distributions, the degrees of freedom should take into account the number of marginals used |

`tailp` |
the exact statistical significance |

`sump` |
sum of the probabilities used for error checking |

`nenum` |
the total number of tables enumerated |

Yu C, Zelterman D (2001) Exact inference for family disease clusters. Commun Stat – Theory Meth 30:2293-2305

Yu C, Zelterman D (2002) Statistical inference for familial disease clusters. Biometrics 58:481-491

Adapted from family.for by Dani Zelterman, 25/7/03

Dani Zelterman, Jing Hua Zhao

`kin.morgan`

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 26 27 28 | ```
## Not run:
# IPF among 203 siblings of 100 COPD patients from Liang KY, SL Zeger,
# Qaquish B. Multivariate regression analyses for categorical data
# (with discussion). J Roy Stat Soc B 1992, 54:3-40
# the degrees of freedom is 15
famtest<-c(
1, 0, 36,
1, 1, 12,
2, 0, 15,
2, 1, 7,
2, 2, 1,
3, 0, 5,
3, 1, 7,
3, 2, 3,
3, 3, 2,
4, 0, 3,
4, 1, 3,
4, 2, 1,
6, 0, 1,
6, 2, 1,
6, 3, 1,
6, 4, 1,
6, 6, 1)
test<-t(matrix(famtest,nrow=3))
famp<-pfc(test)
## End(Not run)
``` |

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