Probability of familial clustering of disease

Usage

pfc.sim(famdata, n.sim = 1e+06, n.loop = 1)

Arguments

famdata: collective information of sib size, number of affected sibs and their frequencies.
n.sim: number of simulations in a single Monte Carlo run.
n.loop: total number of Monte Carlo runs.

Value

The returned value is a list containing:

n.sim a copy of the number of simulations in a single Monte Carlo run.
n.loop the total number of Monte Carlo runs.
p the observed p value.
tailpl accumulated probabilities at the lower tails.
tailpu simulated p values.

Details

To calculate probability of familial clustering of disease using Monte Carlo simulation.

Note

Adapted from runi.for from Change Yu, 5/6/4

References

Yu C, Zelterman D (2001). “Exact inference for family disease clusters.” Communications in Statistics - Theory and Methods, 30(11), 2293-2305. doi:10.1081/STA-100107686 .

Author

Chang Yu, Dani Zelterman

Examples

if (FALSE) { # \dontrun{
# Li FP, Fraumeni JF Jr, Mulvihill JJ, Blattner WA, Dreyfus MG, Tucker MA,
# Miller RW. A cancer family syndrome in twenty-four kindreds.
# Cancer Res 1988, 48(18):5358-62. 

# family_size  #_of_affected frequency

famtest<-c(
1, 0, 2,
1, 1, 0,
2, 0, 1,
2, 1, 4,
2, 2, 3,
3, 0, 0,
3, 1, 2,
3, 2, 1,
3, 3, 1,
4, 0, 0,
4, 1, 2,
5, 0, 0,
5, 1, 1,
6, 0, 0,
6, 1, 1,
7, 0, 0,
7, 1, 1,
8, 0, 0,
8, 1, 1,
8, 2, 1,
8, 3, 1,
9, 3, 1)

test<-matrix(famtest,byrow=T,ncol=3)

famp<-pfc.sim(test)
} # }