the return value of function 'meta.TradPerm', e.g. 'perm_case_11' of certain stuy, 'perm_Qp', 'perm_p' etc.
MC_data
the return value of function 'meta.MCPerm', e.g. 'perm_case_11' of certain stuy, 'perm_Qp', 'perm_p' etc.
Trad_col
the color for cumulative distribution curve of 'Trad_data'. Default value is 'black'.
MC_col
the color for cumulative distribution curve of 'MC_data'. Default value is 'red'.
title
the main title(on top).
xlab,ylab
X axis label. Y axis label, default value is 'cumulative probability'.
Details
Plotting cumulative distribution curve for the return value(e.g. 'perm_case_11' of certain stuy,
'perm_Qp', 'perm_p' etc) of 'meta.TradPerm' and 'meta.MCPerm' is to compare the simulative data distribution
got by TradPerm and MCPerm method whether are same.
MCPerm details see chisq.MCPerm.
TradPerm details see chisq.TradPerm.
Author(s)
Lanying Zhang and Yongshuai Jiang <jiangyongshuai@gmail.com>
References
William S Noble(Nat Biotechnol.2009): How does multiple testing correction work?
## import data
# data(MetaGenotypeData)
## delete first line which contains the names of each column
# temp=MetaGenotypeData[-1,];
# rowNum=nrow(temp)
# gen=matrix(0,nrow=rowNum,ncol=1);
# aff=matrix(0,nrow=rowNum,ncol=1);
# for(j in 1:rowNum){
# gen[j,]=paste(temp[j,14],temp[j,15],sep=" ");
# case_num=length(unlist(strsplit(temp[j,14],split=" ")));
# control_num=length(unlist(strsplit(temp[j,15],split=" ")));
# case_aff=paste(rep(2,case_num),collapse=" ");
# control_aff=paste(rep(1,control_num),collapse=" ");
# aff[j,]=paste(case_aff,control_aff,sep=" ");
# }
# result1=meta.TradPerm(gen,aff,split=" ",sep="/",naString="-",
# model="allele",method="MH",repeatNum=1000)
# result1
## plot study 12
# Trad_case_1=2*result1$perm_case_11[12,]+result1$perm_case_12[12,]
## import data
# data(MetaGenotypeCount)
## delete the first line which is the names for columns.
# temp=MetaGenotypeCount[-1,,drop=FALSE]
# result=meta.MCPerm(case_11=as.numeric(temp[,14]),case_12=as.numeric(temp[,16]),
# case_22=as.numeric(temp[,18]),control_11=as.numeric(temp[,15]),
# control_12=as.numeric(temp[,17]),control_22=as.numeric(temp[,19]),
# model="allele",method="MH",repeatNum=100000)
# result2
## plot study 12
# MC_case_1=2*result2$perm_case_11[12,]+result2$perm_case_12[12,]
# VS.CDC(Trad_case_1,MC_case_1,title="cumulative distribution cure for case_1")
# VS.CDC(result1$perm_Qp,result2$perm_Qp,title="cumulative distribution cure for Qp")
# VS.CDC(result1$perm_p,result2$perm_p,title="cumulative distribution cure for p")