MCPerm (version 1.1.4)

VS.KS: Kolmogorov-Smirnov test for the return value of 'meta.TradPerm' and 'meta.MCPerm'

Description

Kolmogorov-Smirnov test for the return value of 'meta.TradPerm' and 'meta.MCPerm'.

Usage

VS.KS(Trad_data, MC_data, scatter_alpha = 0.01, line_alpha = 0.001,  scatter_col = "black", line_col = "red", 
	 xlab = NULL, ylab = "KS test p_value", 
	 title = "KS (Kolmogorov-Smirnov) test")

Arguments

Trad_data

a matrix with more than one line, each line is the return value of function 'meta.TradPerm'(e.g. 'perm_case_11' of certain study, 'perm_Qp', 'perm_p' etc).

MC_data

a matrix with more than one line, each line is the return value of function 'meta.MCPerm'(e.g. 'perm_case_11' of certain study, 'perm_Qp', 'perm_p' etc).

scatter_alpha

numeric indicates k-s test p.value less 'scatter_alpha' to be plotted. Default value is 0.01.

line_alpha

numeric indicates the threshold value of k-s test significance. Default value is 0.001.

scatter_col

the color of sctter points which to be plotted. Default value is 'black'.

line_col

the color of the line. Default value is 'red'.

xlab,ylab

X axis label. Y axis label, default value is 'KS test p_value'.

title

the main title(on top),default value is "KS (Kolmogorov-Smirnov) test".

Value

KS_p

a numeric vector, p.value of K-S test.

Details

Kolmogorov-Smirnov test for the return value(e.g. 'perm_case_11', 'perm_Qp' etc.) of 'meta.TradPerm' and 'meta.MCPerm' to test the corresponding form the whole. Just plotting p.value less the 'scatter_alpha' will more clear to see the number of no-corresponding data.

References

The two-sided one-sample distribution comes via Marsaglia, Tsang and Wang (2003).

See Also

meta.MCPerm, meta.TradPerm, chisq.MCPerm, chisq.TradPerm, VS.Hist, VS.CDC, VS.QQ VS.Allele.Hist, VS.Genotype.Hist

Examples

## write the return value 'perm_case_11','perm_Qp' or 'perm_p' of function 'meta.TradPerm' 
  # to file 'Trad_data.txt'
## write the return value 'perm_case_11','perm_Qp' or 'perm_p' of function 'meta.MCPerm' 
  # to file 'MC_data.txt'
## when all meta analysis run over, read the file to K-S test
## if the line of file is separated by '\t'
# Trad=read.table("Trad_data.txt",sep="\t",header=FALSE)
# MC=read.table("MC_data.txt",sep="\t",header=FALSE)

# VS.KS(Trad, MC)