R: plot quantile-quantile plot for the return value of...
VS.QQ
R Documentation
plot quantile-quantile plot for the return value of 'meta.TradPerm'
and 'meta.MCPerm' for certain study or meta analysis.
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 <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.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")