FDRplot | R Documentation |
This function plots FDR point and CI estimates over a sequence of possible significance thresholds. Results from fdrTbl() can be plotted directly as input to FDRplot.
FDRplot( plotdat, lowerbound, upperbound, ymax = 1, annot = "", xpos = 0.8, ypos = 0.8 )
plotdat |
a table that is returned from fdrTbl(), or results formated in the same way. |
lowerbound |
-log10(p-value) lower bound for the x-axis of the plot. |
upperbound |
-log10(p-value) upper bound for the x-axis of the plot. |
ymax |
upper limit for range of the y-axis. |
annot |
annotation text to be added to plot area. |
xpos |
x-axis position for annot |
ypos |
y-axis position for annot |
ggplot2 object
Joshua Millstein, joshua.millstein@usc.edu
Joshua Millstein
Millstein J, Volfson D. 2013. Computationally efficient permutation-based confidence interval estimation for tail-area FDR. Frontiers in Genetics | Statistical Genetics and Methodology 4(179):1-11.
Millstein J, Volfson D. 2013. Computationally efficient permutation-based confidence interval estimation for tail-area FDR. Frontiers in Genetics | Statistical Genetics and Methodology 4(179):1-11.
ss = 100 nvar = 100 X = as.data.frame(matrix(rnorm(ss*nvar),nrow=ss,ncol=nvar)) e = as.data.frame(matrix(rnorm(ss*nvar),nrow=ss,ncol=nvar)) Y = .1*X + e nperm = 10 myanalysis = function(X,Y){ ntests = ncol(X) rslts = as.data.frame(matrix(NA,nrow=ntests,ncol=2)) names(rslts) = c("ID","pvalue") rslts[,"ID"] = 1:ntests for(i in 1:ntests){ fit = cor.test(X[,i],Y[,i],na.action="na.exclude", alternative="two.sided",method="pearson") rslts[i,"pvalue"] = fit$p.value } return(rslts) } # End myanalysis # Generate observed results obs = myanalysis(X,Y) # Generate permuted results perml = vector('list',nperm) for(perm in 1:nperm){ X1 = X[order(runif(nvar)),] perml[[perm]] = myanalysis(X1,Y) } # FDR results table myfdrtbl = fdrTbl(obs$pvalue,perml,"pvalue",nvar,0,3) # Plot results FDRplot(myfdrtbl,0,3,annot="A. An Example")
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