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gap

pfc

Probability of familial clustering of disease

Description

To calculate exact probability of familial clustering of disease

Usage

pfc(famdata,enum)

Arguments

`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

Value

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

References

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

Note

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

Author(s)

Dani Zelterman, Jing Hua Zhao

Examples

## 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)

gap

Genetic Analysis Package

v1.2.3-1

GPL (>= 2)

Authors

Jing Hua Zhao and colleagues with inputs from Kurt Hornik and Brian Ripley

Initial release

2021-4-21

pfc