MCPerm (version 1.1.4)

VS.Genotype.CDC: separately plot cumulative distribution curve for the return value(genotype count) of 'meta.TradPerm' and 'meta.MCPerm' for certain study

Description

Separately plot cumulative distribution curve for the return value(genotype count) of 'meta.TradPerm' and 'meta.MCPerm' for certain study.

Usage

VS.Genotype.CDC(Trad_case_11, Trad_case_12, Trad_case_22,  Trad_control_11, Trad_control_12, Trad_control_22, 
	 MC_case_11, MC_case_12, MC_case_22, 
	 MC_control_11, MC_control_12, MC_control_22,
	 Trad_col = "black", MC_col = "red", 
	 title = NULL, xlab = "Genotype count", ylab = "cumulative probability")

Arguments

Trad_case_11

a numeric vector, the return value 'perm_case_11' got by meta.TradPerm method for certain study.

Trad_case_12

a numeric vector, the return value 'perm_case_12' got by meta.TradPerm method for certain study.

Trad_case_22

a numeric vector, the return value 'perm_case_22' got by meta.TradPerm method for certain study.

Trad_control_11

a numeric vector, the return value 'perm_control_11' got by meta.TradPerm method for certain study.

Trad_control_12

a numeric vector, the return value 'perm_control_12' got by meta.TradPerm method for certain study.

Trad_control_22

a numeric vector, the return value 'perm_control_22' got by meta.TradPerm method for certain study.

MC_case_11

a numeric vector, the return value 'perm_case_11' got by meta.MCPerm method for certain study.

MC_case_12

a numeric vector, the return value 'perm_case_12' got by meta.MCPerm method for certain study.

MC_case_22

a numeric vector, the return value 'perm_case_22' got by meta.MCPerm method for certain study.

MC_control_11

a numeric vector, the return value 'perm_control_11' got by meta.MCPerm method for certain study.

MC_control_12

a numeric vector, the return value 'perm_control_12' got by meta.MCPerm method for certain study.

MC_control_22

a numeric vector, the return value 'perm_control_22' got by meta.MCPerm method for certain study.

Trad_col

the color of cumulative distribution cure for Trad_case_11/Trad_case_12/Trad_case_22/ Trad_control_11/Trad_control_12/Trad_control_22. Default value is 'black'.

MC_col

the color of cumulative distribution cure for MC_case_11/MC_case_12/MC_case_22/ MC_control_11/MC_control_12/MC_control_22. Default value is 'red'.

title

the main title(on top).

xlab,ylab

X axis label, default value is "Genotype count". Y axis label, default value is "cumulative probability".

Details

Separately plotting cumulative distribution curve for the return value(genotype count) of 'meta.TradPerm' and 'meta.MCPerm' for certain study is to compare the simulative genotype count distribution got by TradPerm and MCPerm method whether are same.

MCPerm details see chisq.MCPerm. TradPerm details see chisq.TradPerm.

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.CDC, VS.KS, VS.Genotype.Hist, VS.Genotype.QQ, VS.Allele.CDC, PermMeta.LnOR.CDC

Examples

## 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

## 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
# VS.Genotype.CDC(result1$perm_case_11[12,],result1$perm_case_12[12,],result1$perm_case_22[12,],
    # result1$perm_control_11[12,],result1$perm_control_12[12,],result1$perm_control_22[12,],
	 # result2$perm_case_11[12,],result2$perm_case_12[12,],result2$perm_case_22[12,],
	 # result2$perm_control_11[12,],result2$perm_control_12[12,],result2$perm_control_22[12,],
    # Trad_col="grey",MC_col="black", title="hist_plot for six genotype")