qqnorm plot for the return value 'perm_LnOR' of 'meta.MCPerm' or 'meta.TradPerm'

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Description

qqnorm plot for the return value 'perm_LnOR' of 'meta.MCPerm' or 'meta.TradPerm'.

Usage

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PermMeta.LnOR.qqnorm(PermMeta, plot_study = "all", nrow = 2, ncol = 2, 
    main = "qqnorm plot for LnOR", title = NULL, 
	 xlab = "Theoretical Quantiles", ylab = "Sample Quantiles", 
	 scatter_col = "black", line_col = "red")

Arguments

PermMeta

the result of function 'meta.TradPerm' or 'meta.MCPerm'.

plot_study

a numeric vector indicates which study(ies) in meta analysis to be plotted. Default value is 'all', which indicates all studies in meta analysis to be plotted.

nrow,ncol

positive integer, divides the device up into 'nrow' rows and 'ncol' columns. Default value is 2.

main

the main title (on top), default value is "qqnorm plot for LnOR".

title

the sub main title for each plotted study(on top).

xlab,ylab

X axis label, default value is "Theoretical Quantiles". Y axis label, default value is "Sample Quantiles".

scatter_col

the color of the scatter points. Default value is 'black'.

line_col

the color of the line which passes through the normal distribution probs quantiles, the first and third quartiles. Default value is 'red'.

Details

Plotting a normal QQ plot for simulative log odd ratios is to test that simulative data is whether fit normal distribution, Snd plot a line which passes through the normal distribution probs quantiles, the first and third quartiles.

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

Author(s)

Lanying Zhang and Yongshuai Jiang <jiangyongshuai@gmail.com>

See Also

meta.MCPerm, meta.TradPerm, chisq.MCPerm, chisq.TradPerm, VS.QQ, VS.KS, VS.Genotype.QQ, VS.Allele.QQ, PermMeta.LnOR.Hist, PermMeta.LnOR.CDC, PermMeta.LnOR.boxplot, PermMeta.Hist, PermMeta.boxplot

Examples

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## import data
# data(MetaGenotypeCount)
## delete first line
# temp=MetaGenotypeCount[-1,];
# 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",fixed_method="MH",random_method="DL",repeatNum=1000)
# PermMeta.LnOR.qqnorm(result,plot_study=c(1,4,9,15),nrow=2,ncol=2)

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