VS.QQ: plot quantile-quantile plot for the return value of...

In MCPerm: A Monte Carlo permutation method for multiple test correlation

Description

plot quantile-quantile plot for the return value of 'meta.TradPerm' and 'meta.MCPerm' for certain study or meta analysis.

Usage

VS.QQ(Trad_data, MC_data, scatter_col = "black", line_col = "black", title = "QQ plot", 
    xlab = "Quantile for TradPerm data)", ylab = "Quantile for MCPerm data")

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.
scatter_col	the color for scatter points of quantile-quantile plot, default value is 'black'.
line_col	the color of line which passes through the sample distribution probs quantiles, the first and third quartiles. Default value is 'black'.
title	the main title(on top), default value is 'QQ plot'.
xlab,ylab	X axis label, default is "Quantile for TradPerm data)". Y axis label, default is "Quantile for MCPerm data".

Details

Plotting quantile-quantile plot 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 <[e-mail]>

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.CDC, VS.KS, VS.Allele.QQ, VS.Genotype.QQ

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
## 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.QQ(Trad_case_1,MC_case_1,title="cumulative distribution cure for case_1")
# VS.QQ(result1$perm_Qp,result2$perm_Qp,title="cumulative distribution cure for Qp")
# VS.QQ(result1$perm_p,result2$perm_p,title="cumulative distribution cure for p")

MCPerm documentation built on May 29, 2017, 11:27 a.m.