Description Usage Arguments Details Author(s) References See Also Examples
plot cumulative distribution curve for the return value of 'meta.TradPerm' and 'meta.MCPerm' for certain study or meta analysis
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Trad_data |
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'. |
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
.
Lanying Zhang and Yongshuai Jiang <jiangyongshuai@gmail.com>
William S Noble(Nat Biotechnol.2009): How does multiple testing correction work?
Edgington. E.S.(1995): Randomization tests, 3rd ed.
meta.MCPerm
,
meta.TradPerm
,
chisq.MCPerm
,
chisq.TradPerm
,
VS.Hist
,
VS.QQ
,
VS.KS
,
VS.Allele.Hist
,
VS.Genotype.Hist
,
PermMeta.LnOR.Hist
,
PermMeta.Hist
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | ## 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")
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