View source: R/qqplot.pvalues.r
| qqplot.pvalues | R Documentation |
Draws a QQ plot of p-values
qqplot.pvalues(p, col.abline = "red", CB = TRUE, col.CB = "gray80",
CB.level = 0.95, thinning = TRUE, ...)
p |
A vector of p-values, or a data.frame with a column named |
col.abline |
Color of the line of slope 1. Set to |
CB |
|
col.CB |
The color of the confidence band. |
CB.level |
The level of the confidence band. |
thinning |
|
... |
Graphical parameters to be passed to |
The QQ plot is on the -\log_{10} scale, as is usual when reporting
GWAS results.
The confidence band is not a global confidence region: it is the mere juxtaposition
of confidence intervals for each quantile. Moreover it assumes independance of the
p-values, an hypothesis hich is false for the p-values resulting from an association
test in presence of linkage disequilibrium. Therefore, the probability that some of the
points lie outsite of this band is greater that CB.level.
The thinning procedure suppress some points to avoid generating too heavy graphs. The user
should check that setting thinning = FALSE does not change the final aspect of the
QQ plot.
association.test, manhattan, qqplot,
plot.default, points.default
# a vector of uniform p-values
p <- runif(1e6)
qqplot.pvalues(p)
# if we don't thin the points, using pch = "." is advised
qqplot.pvalues(p, pch = ".", cex = 2, thinning = FALSE)
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