ASPBay (version 1.2)

Graphs.Bayesian: Graphs

Description

Plot graphs to visualize the results of ASP.Bayesian

Usage

Graphs.Bayesian(M, burn=0, xbins=200, ORlim=c(1,5), conf.int=c(0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.95), print=TRUE)

Arguments

M

Object given by the function ASP.Bayesian

burn

The first burn values of the sampling are removed

xbins

The number of bins which partition the range of graph variables

ORlim

OR limits in graphs

conf.int

Chosen credibility intervals

print

Logical, if TRUE the plots are printed

Value

hex_r2_OR

Hexbinplot object with the linkage disequilibrium between observed and causal SNPs in abscissae and the OR of causal SNP in ordinates.

hex_fa_fb

Hexbinplot object with the alternative allele frequency of causal SNP in abscissae and the alternative allele frequency of observed SNP in ordinates.

Details

Plot two graphs and give associated hexbinplot objects. This two graphs summarize the results of the Bayesian method. The first graph shows the linkage disequilibrium between observed and causal SNPs in abscissae and the OR of causal SNP in ordinates. The second graph displays the alternative allele frequency of causal SNP in abscissae and the alternative allele frequency of observed SNP in ordinates. Before plotting the graphs, the causal odds ratio is transformed. The value of OR is kept if it is superior to 1, otherwise it is inversed. The alternative causal allele frequency is transformes accordingly: if the OR is inferior to 1, the frequency is replaced by its complement to 1. With this transformations, we avoid to obtain two peaks corresponding to equivalent parameter values.

References

Dandine-Roulland, Claire and Perdry, Herve. Where is the causal variant? On the advantage of the family design over the case-control design in genetic association studies. Submitted to Eur J Hum Genet

See Also

ASP.Bayesian

Examples

data(ASPData)
B <- ASP.Bayesian(1e5, ASPData$Control, ASPData$Index,
                  ASPData$IBD, 15)
G <- Graphs.Bayesian(B, burn = 5000, xbins=100)