Compute error rates for a given model.

1 | ```
error.rates(H0,Z, pMc, geno, no.ca, no.con=nrow(geno), sim.no = 1000)
``` |

`H0` |
the index number for a given model for functional SNPs |

`Z` |
number of functional SNPs for the given model |

`pMc` |
array of allele frequencies of case samples |

`geno` |
matrix of alleles, such that each locus has a pair of adjacent columns of alleles, and the order of columns corresponds to the order of loci on a chromosome. If there are K loci, then ncol(geno) = 2*K. Rows represent the alleles for each subject. Each allele shoud be represented as numbers (A=1,C=2,G=3,T=4). |

`no.ca` |
number of case chromosomes |

`no.con` |
number of control chromosomes |

`sim.no` |
number of simulations for error rates estimation |

array of results consisted of Type I error rate (alpha=0.05), Type I error rate (alpha=0.01), Type II error rate (beta=0.05), Type II error rate (beta=0.01), percent when the target model has the lowest corrected -2 log likelihood ratio.

allele.freq hap.freq lrtB

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
## LRT tests when SNP1 & SNP6 are the functional polymorphisms.
data(apoe)
n<-c(2000, 2000, 2000, 2000, 2000, 2000, 2000) #case sample size = 1000
x<-c(1707, 281,1341, 435, 772, 416, 1797) #allele numbers in case samples
Z<-2 #number of functional SNPs for tests
n.poly<-ncol(apoe7)/2 #total number of SNPs
#index number for the model in this case is 5 for SNP1 and 6.
#apoe7 is considered to represent the true control allele and haplotype frequencies.
#Control sample size = 1000.
error.rates(5, 2, x/n, apoe7, 2000, 2000, sim.no=2)
# to obtain valid rates, use sim.no=1000.
``` |

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