plot cumulative distribution curve for the return value of 'meta.TradPerm' and 'meta.MCPerm' for certain study or meta analysis

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Description

plot cumulative distribution curve for the return value of 'meta.TradPerm' and 'meta.MCPerm' for certain study or meta analysis

Usage

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VS.CDC(Trad_data, MC_data, Trad_col = "black", MC_col = "red", 
    title = NULL, xlab = NULL, ylab = "cumulative probability")

Arguments

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'.

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?

Edgington. E.S.(1995): Randomization tests, 3rd ed.

See Also

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

Examples

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## 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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