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qqunif
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## pvalue vs uniform | |
qqunif = | |
function(p,BH=T,CI=T,mlog10_p_thres=30,...) | |
{ | |
## thresholded by default at 1e-30 | |
p=na.omit(p) | |
nn = length(p) | |
xx = -log10((1:nn)/(nn+1)) | |
p_thres = 10^{-mlog10_p_thres} | |
if( sum( p < p_thres) ) | |
{ | |
warning(paste("thresholding p to ",p_thres) ) | |
p = pmax(p, p_thres) | |
} | |
plot( xx, -sort(log10(p)), | |
xlab=expression(Expected~~-log[10](italic(p))), | |
ylab=expression(Observed~~-log[10](italic(p))), | |
cex.lab=1.4,mgp=c(2,1,0), | |
... ) | |
abline(0,1,col='gray') | |
if(BH) | |
{ | |
abline(-log10(0.05),1, col='orange',lty=1) | |
abline(-log10(0.10),1, col='orange',lty=2) | |
abline(-log10(0.25),1, col='orange',lty=3) | |
legend('topleft', c("FDR = 0.05","FDR = 0.10","FDR = 0.25"), | |
col=c('orange','orange','orange'),lty=1:3, cex=1) | |
abline(h=-log10(0.05/nn)) ## bonferroni | |
} | |
if(CI) | |
{ | |
## create the confidence intervals | |
c95 <- rep(0,nn) | |
c05 <- rep(0,nn) | |
## the jth order statistic from a | |
## uniform(0,1) sample | |
## has a beta(j,n-j+1) distribution | |
## (Casella & Berger, 2002, | |
## 2nd edition, pg 230, Duxbury) | |
## this portion was posted by anonymous on | |
## http://gettinggeneticsdone.blogspot.com/2009/11/qq-plots-of-p-values-in-r-using-ggplot2.html | |
for(i in 1:nn) | |
{ | |
c95[i] <- qbeta(0.95,i,nn-i+1) | |
c05[i] <- qbeta(0.05,i,nn-i+1) | |
} | |
lines(xx,-log10(c95),col='gray') | |
lines(xx,-log10(c05),col='gray') | |
} | |
} | |
## to add other qqplots on top of current plot | |
qqpoints = | |
function(p,BH=T,mlog10_p_thres=30,...) | |
{ | |
## thresholded by default at 1e-30 | |
p=na.omit(p) | |
nn = length(p) | |
xx = -log10((1:nn)/(nn+1)) | |
p_thres = 10^{-mlog10_p_thres} | |
if( sum( p < p_thres) ) | |
{ | |
warning(paste("thresholding p to ",p_thres) ) | |
p = pmax(p, p_thres) | |
} | |
nn = length(p) | |
xx = -log10((1:nn)/(nn+1)) | |
points( xx, -sort(log10(p)), ... ) | |
} | |
## to use with dplyr's pipes | |
qqunif.pipe = function(data,...){ | |
pval = data$pval | |
qqunif(pval,...) | |
} | |
qqunif.compare = function(pvec1,pvec2,pthres=NULL,BF2=FALSE,col2='blue',cex2=1,...) | |
{ | |
pvec1 = na.omit(pvec1) | |
pvec2 = na.omit(pvec2) | |
if(!is.null(pthres)) {pvec1 = pmax(pvec1,pthres); pvec2 = pmax(pvec2,pthres)} | |
qqunif(pvec1,...) | |
qqpoints(pvec2,col=col2,pch='+',cex=cex2) | |
if(BF2) abline(h=-log10(0.05/length(pvec2)),col='blue',lw=2) | |
} | |
## testing | |
qqunif_maxp = | |
function(p,BH=T,CI=T,mlog10_p_thres=30,maxp=1,...) | |
{ | |
## thresholded by default at 1e-30 | |
p=na.omit(p) | |
nn = length(p) | |
xx = -log10( (1:nn)*maxp / (nn+1) ) | |
p_thres = 10^{-mlog10_p_thres} | |
if( sum( p < p_thres) ) | |
{ | |
warning(paste("thresholding p to ",p_thres) ) | |
p = pmax(p, p_thres) | |
} | |
plot( xx, -sort(log10(p)), | |
xlab=expression(Expected~~-log[10](italic(p))), | |
ylab=expression(Observed~~-log[10](italic(p))), | |
cex.lab=1.4,mgp=c(2,1,0), | |
... ) | |
abline(0,1,col='gray') | |
if(BH) | |
{ | |
abline(-log10(0.05),1, col='red',lty=1) | |
abline(-log10(0.10),1, col='orange',lty=2) | |
abline(-log10(0.25),1, col='yellow',lty=3) | |
legend('topleft', c("FDR = 0.05","FDR = 0.10","FDR = 0.25"), | |
col=c('red','orange','yellow'),lty=1:3, cex=1) | |
abline(h=-log10(0.05/nn)) ## bonferroni | |
} | |
if(CI) | |
{ | |
## create the confidence intervals | |
c95 <- rep(0,nn) | |
c05 <- rep(0,nn) | |
## the jth order statistic from a | |
## uniform(0,1) sample | |
## has a beta(j,n-j+1) distribution | |
## (Casella & Berger, 2002, | |
## 2nd edition, pg 230, Duxbury) | |
## this portion was posted by anonymous on | |
## http://gettinggeneticsdone.blogspot.com/2009/11/qq-plots-of-p-values-in-r-using-ggplot2.html | |
for(i in 1:nn) | |
{ | |
c95[i] <- qbeta(0.95,i,nn-i+1)*maxp ## CHECK THIS IS RIGHT | |
c05[i] <- qbeta(0.05,i,nn-i+1)*maxp | |
} | |
lines(xx,-log10(c95),col='gray') | |
lines(xx,-log10(c05),col='gray') | |
} | |
} |
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To double check, here it presents the 90% CI?